Oil field industry financial index data pushing method, device, equipment and medium

Abstract

The invention relates to the field of petroleum data processing, and discloses an oilfield industry and financial index data pushing method, device, equipment and medium, an application dimension list can be created according to received oilfield industry and financial index data, the application dimension list comprises one-to-one correspondence between a plurality of application dimensions and a plurality of same-dimension index name groups, and the application dimensions are matched with the same-dimension index name groups. The same-dimension index name group comprises a plurality of first index names. And performing dimension reduction aggregation on the index result original data corresponding to each first index name to obtain dimension reduction aggregated data corresponding to each first index name. And creating a corresponding column type storage structure table according to the same-dimension index name group, storing the dimension-reduced and aggregated data corresponding to each first index name in the same-dimension index name group into the column type storage structure table, and pushing the data to a target system. According to the invention, occupation and consumption of network bandwidth resources can be reduced by constructing the column type storage structure table corresponding to the same-dimension index name group and pushing the column type storage structure table to the target system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil data processing, and in particular to an oil field industry and finance index data pushing method, device, equipment and medium. BACKGROUND

[0002] In the oil industry, industry and finance index data such as single well operation cost, total cost per ton of oil, and economic recoverable rate of reserves can quantify resource input-output ratio, guide enterprises to optimize investment distribution and reduce invalid cost.

[0003] Related technologies can provide industry and finance index data to industry and finance integrated application systems for further processing and use of industry and finance index data to support scientific decision-making and management of enterprises. Specifically, related technologies provide industry and finance index data to industry and finance integrated application systems in the form of single index table pushing, that is, related data of each single index in the industry and finance index data is pushed to the industry and finance integrated application system in the form of a storage table, and then the industry and finance integrated application system extracts the required index data according to business needs and fills out various reports to support industry and finance related business use.

[0004] However, the whole table pushing needs to transmit complete table data, and related technologies provide industry and finance index data to industry and finance integrated application systems in the form of single index table pushing, which needs to occupy a large amount of network bandwidth resources, and the network bandwidth resources are consumed. SUMMARY

[0005] The present application provides an oil field industry and finance index data pushing method, device, equipment and medium to solve the defect that related technologies provide industry and finance index data to industry and finance integrated application systems in the form of single index table pushing, which needs to occupy a large amount of network bandwidth resources and consume a large amount of network bandwidth resources. By constructing a columnar storage structure table corresponding to the same dimension index name group and pushing it to the target system, the occupation and consumption of network bandwidth resources are reduced.

[0006] In a first aspect, the present application provides an oil field industry and finance index data pushing method, comprising: creating an application dimension list according to received oil field industry and finance index data, the application dimension list including a one-to-one correspondence between a plurality of application dimensions and a plurality of same dimension index name groups, and the same dimension index name group including a plurality of first index names; respectively dimensionally reducing and aggregating index result original data corresponding to each first index name to obtain dimensionally reduced and aggregated data corresponding to each first index name; For any one of the same dimension index name groups, a corresponding columnar storage structure table is created according to the same dimension index name group, and the dimension-reduced and aggregated data corresponding to each of the first index names in the same dimension index name group is stored in the columnar storage structure table; The columnar storage structure table corresponding to each of the same dimension index name groups is pushed to a target system.

[0007] Optionally, the application dimension list is created according to the received oilfield industry and finance index data, including: An application request list is created according to the oilfield industry and finance index data, and the application request list includes a one-to-one correspondence relationship between a plurality of second index names and a plurality of application dimensions; For any one of the second index names, the business dimension corresponding to the second index name is obtained, and if the application dimension corresponding to the second index name is a proper subset of the business dimension, the second index name is taken as one of the first index names; Each of the first index names corresponding to the same application dimension is taken as one of the same dimension index name groups, and the application dimension corresponding to the first index names in the same dimension index name group is determined as the application dimension corresponding to the same dimension index name group; The application dimension list is created according to each of the same dimension index name groups and the corresponding application dimension.

[0008] Optionally, the index result original data corresponding to the first index name includes a plurality of row data, and each of the row data includes the first index name, a dimension value under a business dimension, and an index value; The dimension-reduced and aggregated data corresponding to each of the first index names is obtained by respectively dimension-reducing and aggregating the index result original data corresponding to each of the first index names, including: For any one of the first index names, a target dimension other than a first application dimension is determined in the business dimension corresponding to the first index name, the first application dimension is the application dimension corresponding to the first index name, a plurality of row data to be aggregated is found in the index result original data corresponding to the first index name, the dimension value under the first application dimension in each of the row data to be aggregated is the same and the dimension value under the target dimension is different, the index values in the plurality of row data to be aggregated are added or average value calculated to obtain an aggregated index value, the plurality of row data to be aggregated is aggregated into new row data, and the target dimension and the corresponding dimension value are deleted to obtain the dimension-reduced and aggregated data corresponding to the first index name, the new row data includes the first index name, the dimension value under the first application dimension, and the aggregated index value.

[0009] Optionally, the creating a columnar storage structure table corresponding to the same-dimension indicator name group comprises: creating a first column name corresponding to the first application dimension according to the first indicator name in the same-dimension indicator name group, and creating a second column name corresponding to each of the first indicator names in the same-dimension indicator name group. creating a table header comprising the first column name and the second column name according to the first column name and the second column name, and creating a plurality of blank row data under the table header, to obtain a columnar storage structure table corresponding to the same-dimension indicator name group.

[0010] Optionally, the storing the reduced-dimension aggregated data corresponding to each of the first indicator names in the same-dimension indicator name group into the columnar storage structure table comprises: for the reduced-dimension aggregated data corresponding to any of the first indicator names in the same-dimension indicator name group, determining target blank row data to be stored in the columnar storage structure table, storing the dimension value under the first application dimension in the reduced-dimension aggregated data into a position under the first column name in the target blank row data, and storing the indicator value in the reduced-dimension aggregated data into a position under the second column name in the target blank row data.

[0011] Optionally, after the obtaining the business dimension corresponding to the second indicator name, the method further comprises: if the application dimension corresponding to the second indicator name comprises a dimension different from the business dimension, determining that the indicator result original data corresponding to the second indicator name is error data, and prohibiting the second indicator name from being used as the first indicator name.

[0012] Optionally, after the obtaining the business dimension corresponding to the second indicator name, the method further comprises: if the application dimension corresponding to the second indicator name is consistent with the business dimension corresponding to the second indicator name, determining the same-dimension indicator name group comprising the second indicator name, and storing the indicator result original data corresponding to each of the second indicator names in the same-dimension indicator name group into a corresponding columnar storage structure table.

[0013] In a second aspect, the present application provides an oilfield industry financial indicator data pushing device, comprising: The first creation unit is used to create an application dimension list based on the received oilfield business and financial indicator data. The application dimension list includes a one-to-one correspondence between multiple application dimensions and multiple groups of indicator names of the same dimension. The group of indicator names of the same dimension includes multiple first indicator names. The dimension reduction and aggregation unit is used to perform dimension reduction and aggregation on the original data of the indicator results corresponding to each first indicator name to obtain the dimension reduction and aggregation data corresponding to each first indicator name. The second creation unit is used to create a corresponding columnar storage structure table for any group of indicator names of the same dimension. Storage unit, used to store the dimension-reduced aggregated data corresponding to each of the first indicator names in the same dimension indicator name group into the columnar storage structure table; The push unit pushes the columnar storage structure table corresponding to each of the same dimension indicator name groups to the target system.

[0014] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the oilfield financial indicator data push method of the first aspect or any corresponding embodiment described above.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the oilfield financial indicator data push method described in the first aspect or any corresponding embodiment thereof.

[0016] The oilfield business and financial indicator data push method, apparatus, equipment, and medium provided by this invention can create an application dimension list based on the received oilfield business and financial indicator data. The application dimension list includes a one-to-one correspondence between multiple application dimensions and multiple groups of indicator names within the same dimension. Each group of indicator names within the same dimension includes multiple first indicator names. Dimensionality reduction and aggregation are performed on the original data corresponding to each first indicator name to obtain the dimensionality-reduced and aggregated data for each first indicator name. For any group of indicator names within the same dimension, a corresponding columnar storage structure table is created based on the group, and the dimensionality-reduced and aggregated data corresponding to each first indicator name in the same dimension group is stored in the columnar storage structure table. The columnar storage structure table corresponding to each group of indicator names is then pushed to the target system. This invention, by constructing columnar storage structure tables corresponding to groups of indicator names within the same dimension and pushing them to the target system, effectively provides business and financial indicator data to the target system, reducing the occupation and consumption of network bandwidth resources. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for pushing oilfield financial indicators data, provided in an embodiment of the present invention; Figure 2 An application request list provided for embodiments of the present invention; Figure 3 This is a partial set of raw data for the results of an indicator provided in an embodiment of the present invention. Figure 4 This invention provides a dimension-reduced and aggregated data embodiment; Figure 5 This invention provides a definition of data items in a columnar storage table structure. Figure 6 This is another type of dimension-reduced aggregated data provided in an embodiment of the present invention; Figure 7 This invention provides a columnar storage table for storing data. Figure 8 This is a schematic diagram of the structure of an oilfield financial indicator data push device provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0020] The following is combined Figures 1-7 This invention describes a method for pushing oilfield financial indicator data.

[0021] like Figure 1 As shown in the figure, this embodiment proposes a first method for pushing oilfield financial indicator data, which may include the following steps: S101. Create an application dimension list based on the received oilfield financial indicator data. The application dimension list includes a one-to-one correspondence between multiple application dimensions and multiple groups of indicator names in the same dimension. The groups of indicator names in the same dimension include multiple first indicator names.

[0022] Among them, the oilfield business and financial indicators data can be the business and financial indicators data generated during the exploration and development of oil and gas fields.

[0023] Specifically, the first indicator name can be the name of a single indicator.

[0024] It should be noted that each primary indicator name has a corresponding application dimension. Different primary indicator names may correspond to the same or different application dimensions.

[0025] like Figure 2 As shown, the first indicator names in the oilfield's financial and operational data include: number of wells drilled in the current month, number of wells completed in the current month, average well depth of wells completed in the current month, average drilling cycle of wells completed in the current month, total well construction cycle of wells completed in the current month, average well construction cycle of wells completed in the current month, average well completion cycle of wells completed in the current month, total well completion cycle of wells completed in the current month, average drilling production time in the current month, average drilling non-production time in the current month, cumulative two-dimensional workload, and cumulative three-dimensional workload. The application dimensions corresponding to different first indicator names can be the same. For example, the application dimensions corresponding to the number of wells drilled in the current month and the number of wells completed in the current month are the same: branch company, year, month, and first-level well. The application dimensions corresponding to the number of wells drilled in the current month and the cumulative two-dimensional workload are different.

[0026] The same dimension indicator name group can include multiple first indicator names, and each first indicator name corresponds to the same application dimension.

[0027] Specifically, this embodiment can be applied to a data lake platform. This embodiment can receive oilfield business and financial indicator data sent by the application request end, determine the application dimension corresponding to each first indicator name in the oilfield business and financial indicator data, and classify each first indicator name corresponding to the same application dimension into a same dimension indicator name group.

[0028] Optionally, step S101 includes: An application request list is created based on oilfield financial indicators. The application request list includes multiple secondary indicator names and one-to-one correspondences between multiple application dimensions. For any second indicator name, obtain the business dimension corresponding to the second indicator name. If the application dimension corresponding to the second indicator name is a proper subset of the business dimension, then the second indicator name is used as a first indicator name. Each first indicator name corresponding to the same application dimension is treated as a group of indicator names of the same dimension, and the application dimension corresponding to the first indicator name in the group of indicator names of the same dimension is determined as the application dimension corresponding to the group of indicator names of the same dimension. Create an application dimension list based on each group of metric names within the same dimension and its corresponding application dimension.

[0029] Specifically, the second indicator name is the name of a single indicator.

[0030] The application request list in this embodiment can be as follows: Figure 2 As shown, each second indicator name has a corresponding application dimension.

[0031] Specifically, in this embodiment, for any second indicator name, the corresponding business dimension can be determined in the standard specification. If the application dimension corresponding to the second indicator name is a proper subset of the business dimension, then the second indicator name can be determined as a first indicator name. For example, if the business dimension corresponding to the second indicator name "number of wells completed in the current month" is "branch company, year, month, first-level well, third-level well", and the application dimension corresponding to "number of wells completed in the current month" is "branch company, year, month, first-level well", then it can be determined that the application dimension corresponding to "number of wells completed in the current month" is a proper subset of the business dimension, and therefore the second indicator name "number of wells completed in the current month" can be used as a first indicator name.

[0032] S102. Perform dimensionality reduction and aggregation on the original data of the indicator results corresponding to each first indicator name to obtain the dimensionality reduction and aggregation data corresponding to each first indicator name.

[0033] Specifically, in this embodiment, for any first indicator name, the original data of the indicator result corresponding to the first indicator name can be obtained from the oilfield business and financial indicator data, and the original data of the indicator result corresponding to the first indicator name can be reduced in dimension and aggregated to obtain the data after the reduction and aggregation of the first indicator name.

[0034] Optionally, the raw data of the indicator result corresponding to the first indicator name includes multiple rows of data, and each row of data includes the first indicator name, the dimension value under the business dimension, and the indicator value. Step S102 includes: For any first indicator name, determine the target dimension other than the first application dimension in the business dimension corresponding to the first indicator name. The first application dimension is the application dimension corresponding to the first indicator name. Find multiple rows of data to be aggregated in the original data of the indicator results corresponding to the first indicator name. The dimension values ​​under the first application dimension are the same in each row of data to be aggregated, but the dimension values ​​under the target dimension are different. Add or average the indicator values ​​in the multiple rows of data to be aggregated to obtain the aggregated indicator value. Aggregate the multiple rows of data to be aggregated into a new row of data and delete the target dimension and the corresponding dimension value to obtain the dimension-reduced aggregated data corresponding to the first indicator name. The new row of data includes the first indicator name, the dimension value under the first application dimension, and the aggregated indicator value.

[0035] Specifically, for the first indicator, "Number of wells drilled in the current month," this embodiment can obtain the raw data of its corresponding indicator results, some of which are as follows: Figure 3 As shown. This embodiment can be used for... Figure 3 The data in the dataset undergoes dimensionality reduction and aggregation. Specifically, the application dimension corresponding to "number of wells completed in the current month" is "branch company, year, month, first-level well type," and the business dimension is "branch company, year, month, first-level well type, third-level well type." In this embodiment, the target dimension can be determined as "third-level well type." This embodiment can... Figure 3 In the data shown, the second and third rows are identified as the two rows to be aggregated. The indicator values ​​in these two rows are added together to obtain an aggregated indicator value of 2. These two rows are then aggregated into a new row, and the target dimension and its corresponding dimension values ​​are deleted. Figure 4 The data shown is after dimensionality reduction and aggregation.

[0036] S103. For any group of indicator names in the same dimension, create a corresponding columnar storage structure table based on the group of indicator names in the same dimension.

[0037] Specifically, in this embodiment, a corresponding columnar storage structure table can be created for any group of indicator names in the same dimension.

[0038] Optionally, step S103 includes: Based on the first application dimension corresponding to the first indicator name in the same dimension indicator name group, create a corresponding first column name, which includes the first application dimension; and based on each first indicator name in the same dimension indicator name group, create a corresponding second column name, which includes each first indicator name in the same dimension indicator name group. Based on the first and second column names, create a table header that includes the first and second column names, and create multiple blank rows of data with an expandable number of rows under the table header to obtain a columnar storage structure table corresponding to the same dimension indicator name group.

[0039] Specifically, this embodiment can create a columnar storage structure for indicator result data corresponding to a group of indicator names within the same dimension. Based on the data type and precision of all indicators involved in the application dimension corresponding to the group of indicator names within the same dimension, using the application dimension as the primary key and each indicator value as a general key (data item), a columnar storage structure for indicator result data corresponding to that application dimension is automatically constructed. The type and precision of different data items in the columnar storage structure are defined. For example, for a group of indicator names within the same dimension that includes: number of wells drilled in the current month, number of wells completed in the current month, average well depth of wells completed in the current month, average drilling cycle of wells completed in the current month, total well construction cycle of wells completed in the current month, average well construction cycle of wells completed in the current month, average well completion cycle of wells completed in the current month, total well completion cycle of wells completed in the current month, average drilling production time of wells completed in the current month, and average non-production drilling time of wells completed in the current month, this embodiment can create a columnar storage structure for indicator result data such as: Figure 5 The table structure shown defines the types and precision of different data items in the columnar storage structure.

[0040] S104. Store the dimension-reduced aggregated data corresponding to each first indicator name in the same dimension indicator name group into a columnar storage structure table.

[0041] Optionally, step S104 includes: For any first indicator name in the same dimension indicator name group, the target blank row data to be stored is determined in the columnar storage structure table. The dimension value under the first application dimension in the dimension-reduced aggregated data is stored in the position under the first column name in the target blank row data, and the indicator value in the dimension-reduced aggregated data is stored in the position under the second column name in the target blank row data.

[0042] For example, when the first indicator is named "Number of wells completed in the current month," its corresponding partial dimensionality-reduced aggregated data is as follows: Figure 6 As shown, in this embodiment, the dimension-reduced and aggregated data corresponding to "number of wells completed in the current month" can be stored in a format such as... Figure 7 The columnar storage structure table is shown.

[0043] S105. Push the columnar storage structure table corresponding to each group of indicator names in the same dimension to the target system.

[0044] The target system can be an integrated business and finance application system.

[0045] Specifically, in this embodiment, the columnar storage structure table corresponding to each group of indicator names in the same dimension can be pushed to the business and finance integrated application system.

[0046] Specifically, this embodiment can also invoke the data lake indicator service management function to automatically create indicator data services. The exploration and development data lake indicator service management function is automatically invoked, and based on the columnar storage table of indicator result data corresponding to the current indicator application dimension, it automatically configures and fills in relevant information such as service registration address, service name, request method, and return type, creating a multi-indicator data interface service corresponding to that application dimension, thereby creating an indicator data service that supports batch acquisition of multiple indicators.

[0047] Specifically, this embodiment can also publish indicator data services. The generated indicator data interface service is registered in the data lake's service management platform to realize the publication of the indicator data interface service.

[0048] It should be noted that this embodiment can provide batch data services for oilfield business and financial indicators of the same dimension, which is suitable for the rapid sharing and application of business and financial indicator data in the petroleum industry. It can realize the on-demand and real-time acquisition of business and financial indicator data by the application end, and provide efficient and accurate business and financial indicator data support for the integrated business and financial application system.

[0049] The oilfield business and financial indicator data push method proposed in this embodiment can create an application dimension list based on the received oilfield business and financial indicator data. The application dimension list includes a one-to-one correspondence between multiple application dimensions and multiple groups of indicator names within the same dimension. Each group of indicator names within the same dimension includes multiple first indicator names. Dimensionality reduction and aggregation are performed on the original data corresponding to each first indicator name to obtain the dimensionality-reduced and aggregated data for each first indicator name. For any group of indicator names within the same dimension, a corresponding columnar storage structure table is created based on the group, and the dimensionality-reduced and aggregated data corresponding to each first indicator name in the same dimension group is stored in the columnar storage structure table. The columnar storage structure table corresponding to each group of indicator names is then pushed to the target system. This embodiment achieves the provision of business and financial indicator data to the target system by constructing columnar storage structure tables corresponding to groups of indicator names within the same dimension and pushing them to the target system, thereby reducing the occupation and consumption of network bandwidth resources.

[0050] based on Figure 1 This embodiment proposes a second method for pushing oilfield business and financial indicator data. After obtaining the business dimension corresponding to the second indicator name as described above, this method may further include: If the application dimension corresponding to the second indicator name includes a dimension different from the business dimension, then the original data of the indicator result corresponding to the second indicator name is determined to be erroneous data, and the second indicator name is prohibited from being used as the first indicator name.

[0051] Specifically, in this embodiment, when it is determined that there is a dimension in the application dimension corresponding to the second indicator name that is different from the business dimension, it can be determined that the original data of the indicator result corresponding to the second indicator name is erroneous data, the second indicator name is prohibited from being used as the first indicator name, and an error message is returned to the application requesting end.

[0052] Optionally, after obtaining the business dimension corresponding to the second indicator name, the method may further include: If the application dimension corresponding to the second indicator name is consistent with the business dimension corresponding to the second indicator name, then determine the same dimension indicator name group including the second indicator name, and store the original data of the indicator result corresponding to each second indicator name in the same dimension indicator name group into the corresponding columnar storage structure table.

[0053] Specifically, in this embodiment, when it is determined that the application dimension corresponding to the second indicator name is completely consistent with the business dimension corresponding to the second indicator name, a group of indicator names with the same dimension, including the second indicator name, can be obtained first. Each second indicator name in the group of indicator names with the same dimension corresponds to the same application dimension. Then, in this embodiment, the original data of the indicator results corresponding to each second indicator name in the group of indicator names with the same dimension can be directly stored in the columnar storage structure table corresponding to the group of indicator names with the same dimension.

[0054] It should be noted that the application method of single-indicator whole-table push of relevant technical exploration and development data lake business and financial indicators has the following problems: (1) High network bandwidth consumption; whole-table push requires the transmission of complete table data, which requires a large amount of network bandwidth and is prone to transmission delay or failure; (2) Data redundancy and low transmission efficiency; if multiple indicator data are needed at the same time, and there are the same dimensions between the indicators, the dimension information will have a lot of duplication and redundancy, and the transmission efficiency will also be greatly reduced; (3) Insufficient data real-time performance; single-indicator whole-table push adopts a timed execution mode, which belongs to the supplier's active push, The inability to meet certain business needs of the demand side with high real-time requirements will lead to data lag on the application side. At the same time, due to the lack of incremental synchronization mechanism, the application side needs to frequently load the full amount of data, which affects the response speed. The whole table push cannot provide timely feedback of the latest data. (4) Scalability and flexibility are limited. The single indicator whole table push method makes the application side and the data source strongly coupled. If there are changes in the table structure such as adding fields, all downstream systems need to be synchronized and adapted, resulting in high maintenance costs. In addition, the data range of different business needs is different, but the whole table push cannot be flexibly cut and quickly spliced, resulting in a large amount of redundant data processing.

[0055] It is understood that this embodiment uses... Figure 1The method shown can solve the problems existing in related technologies, reduce the network bandwidth resources occupied during the processing and push of business and financial indicator data, reduce the consumption of network bandwidth resources, improve transmission efficiency, realize the real-time push of business and financial indicator data, improve scalability and flexibility through the creation and application of columnar storage tables, and reduce data redundancy.

[0056] Specifically, this embodiment can automatically receive, parse, and classify application requests. This embodiment can set up and utilize a business and finance indicator service monitoring program to receive application requests for business and finance indicator data from the integrated business and finance system. It then automatically performs structured parsing on the received request data, obtaining all indicator names and corresponding indicator application dimensions from the application request, forming a correspondence list. Next, it classifies the indicators according to the indicator application dimension information, grouping all indicators with the same application dimension into one category, and recording a list of all indicator names corresponding to that application dimension. This establishes a correspondence table between indicator application dimensions and the list of indicator names within the same dimension, thus achieving automatic classification of application requests.

[0057] This implementation can also automatically perform dimensionality reduction and aggregation processing on the indicator result data based on the application dimension. For all application dimensions parsed from the application request, it processes them one by one in a loop, sequentially processing the indicator result data of all indicators involved under each application dimension. If the business dimension of an indicator is larger than the current application dimension, the indicator result data of that indicator is grouped and aggregated using the current application dimension, automatically calculating and generating dimensionality-reduced indicator result data, which is then stored and managed as the basis for subsequent processing. If the business dimension of an indicator is the same as the current application dimension, no processing is performed on the indicator result data of that indicator; it is directly used as the basis for subsequent processing.

[0058] This embodiment can also reconstruct the storage format of indicator result data with the same application dimension by performing row-column conversion. Based on the application dimension of each indicator in the application request, the indicator result data is subjected to dimensionality reduction and aggregation. The organization of the indicator result data storage table after dimensionality reduction and aggregation is consistent with that of the original indicator result data storage table without dimensionality reduction and aggregation, both being a row-based storage method with indicator application dimension, indicator name, and indicator value as data items (each application dimension combination, each indicator corresponding to one row of data). To reduce the problems of redundant data and slow network transmission in this data organization method, this embodiment, based on the data type and data precision of the application dimension and related indicator values ​​in the row-based indicator result storage table, automatically constructs a column-based storage structure for the indicator results corresponding to each application dimension, using each application dimension as the primary key and the indicator values ​​of each indicator with the same application dimension as other data items (i.e., one column of data for each indicator). Then, using data table join query technology, the indicator result storage tables of all indicators with the same application dimension are joined, all indicator result data is reorganized, and the join query results are stored in the newly established indicator result column-based storage table, completing the reconstruction of the indicator result data organization form and realizing the row-column conversion of the indicator result data. This shift in storage method can significantly reduce the amount of data transmitted over the network and improve the efficiency of transmitting indicator data.

[0059] This embodiment can also automatically construct data services for industry and financial indicators of the same dimension. For all indicator application dimensions parsed and processed from user request information, the indicator service management function in the exploration and development data lake indicator management tool is automatically invoked one by one. Based on the indicator result data table reconstructed according to the rows and columns corresponding to each indicator application dimension, the relevant information such as service registration address, service name, request method, and return type is automatically configured and filled in, and a multi-indicator data interface service corresponding to the application dimension is created, so as to realize the batch provision of data services for oilfield industry and financial indicators of the same dimension.

[0060] based on Figure 1 In the third method for pushing oilfield financial indicator data proposed in this embodiment, the process of automatically receiving and processing application requests may include the following steps: S1011. Receive application requests and perform structured parsing. Receive application request data from the application.

[0061] S1012. Locate the first record of the application request list. Locate the first record in the table (application request list) showing the correspondence between the parsed metric name and its application dimension. Starting from this record, process all application request data in a loop.

[0062] Step S1013: Determine whether the application dimension of the current metric is a subset of the corresponding business dimension. Based on the current metric name, retrieve the corresponding business dimension from the data lake, compare the application dimension of the current metric with the business dimension, and determine whether the application dimension is a subset of the business dimension. If it is a subset, proceed to step S1015; otherwise, proceed to step S1014.

[0063] Step S1014: Return the request error message and end. Feedback the error message indicating that the metric's application dimension does not match the business dimension to the application, and terminate the subsequent processing flow.

[0064] Step S1015: Process the current record in the request list. Based on the indicator application dimension information, classify the indicators, grouping all indicators with the same application dimension into one category, and record a list of all indicator names corresponding to that application dimension, establishing a correspondence table between the indicator application dimension and the list of indicator names within the same dimension.

[0065] Step S1016: Has the application request list been completely processed? Determine if the correspondence table between the metric names and their application dimensions parsed in step S1011 (i.e., the application request list) has been completely processed. If yes, proceed to step S1018; otherwise, proceed to step S1017.

[0066] Step S1017: Locate the next record in the application request list. Locate the next record in the table of correspondence between the metric name and its application dimension parsed in step S1011 (i.e., the application request list), and proceed to step S1013 to continue processing the request data in a loop.

[0067] Step S1018: End. Application request processing completed.

[0068] Optionally, the automatic dimensionality reduction and aggregation process for indicator results data may include the following steps: Step S201: Locate the first record in the list of indicator application dimensions. Locate the first record in the table corresponding to the indicator application dimensions and the list of indicator names in the same dimension. Starting from this record, perform dimensionality reduction and aggregation processing on the indicator result data of all indicators corresponding to each indicator application dimension after categorization and merging.

[0069] Step S202: Determine if the entire application dimension list has been processed. Determine if all data in the indicator application dimension list has been processed. If yes, proceed to step S211; otherwise, proceed to step S203.

[0070] Step S203: Locate the first record of the indicator name list corresponding to the current indicator application dimension. In the indicator application dimension list, locate the first record of the indicator name list corresponding to the current indicator application dimension, obtain the indicator name corresponding to the current record, and start from this record to perform dimensionality reduction and aggregation processing on the indicator result data of each indicator in the same application dimension.

[0071] Step S204: Determine if the entire list of indicator names has been processed. Determine if the entire list of indicator names corresponding to the current indicator's application dimension has been processed. If yes, proceed to step S205; otherwise, proceed to step S206.

[0072] Step S205: Locate the next record in the list of indicator application dimensions. Locate the next record in the list of correspondences between indicator application dimensions and indicator names in the same dimension.

[0073] Step S206: Determine whether the application dimension of the current metric is a proper subset of the business dimension corresponding to the metric. Based on the current metric name, obtain the business dimension of the metric as defined in the standard specification from the data lake metric standard, and determine whether the application dimension of the current metric is a proper subset of the business dimension defined in the standard specification. If yes, proceed to step S208; otherwise, proceed to step S207.

[0074] Step S207: Directly obtain the result data of the current indicator. At this point, the application dimension of the current indicator is completely consistent with the business dimension defined in the indicator standard. The indicator result data can be directly obtained from the indicator result table of the current indicator without the need for dimension reduction and aggregation.

[0075] Step S208: Perform dimensionality reduction and aggregation on the result data of the current indicator. Obtain the indicator result data of the current indicator, and regroup and aggregate it using the application dimension of the indicator to calculate and generate the dimensionality-reduced indicator result data. In this embodiment, the grouping and aggregation methods include two types: summation and average calculation.

[0076] Step S209: Unify the storage and organization of indicator result data for the same application dimension. For the original indicator result data or the indicator result data after dimensionality reduction and aggregation obtained from step S207 or step S208, according to the data lake indicator result data storage method, the relevant indicator result data is stored in the corresponding indicator result storage table according to the application dimension corresponding to the current indicator, so as to achieve unified storage and organization of indicator data for the same application dimension.

[0077] Step S210: Locate the next record in the indicator name list. Mark the indicator name corresponding to the current record in the indicator name list corresponding to the current indicator application dimension as processed. Then, locate the next record in the indicator name list corresponding to the current record, obtain the indicator name corresponding to the next record, and then go to step S204 to continue the loop processing.

[0078] Optionally, the process of reconstructing the row and column transformation data of the same application dimension indicator results may include the following steps: Step S301: Locate the first record in the list of indicator application dimensions. Locate the first record in the correspondence table between the indicator application dimensions and the list of indicator names in the same dimension. Starting from this record, reconstruct the data rows and columns of all indicators corresponding to each indicator application dimension after categorization and merging.

[0079] Step S302: Determine if the entire application dimension list has been processed. Determine if all data in the indicator application dimension list has been processed. If yes, proceed to step S307; otherwise, proceed to step S303.

[0080] Step S303: Obtain the indicator result data after dimensionality reduction and aggregation of indicators within the same application dimension. Based on the current indicator application dimension, obtain the indicator result data of all indicators involved in that application dimension from the corresponding dimensionality reduction and aggregation indicator result data table.

[0081] Step S304: Create a columnar storage structure for the indicator result data corresponding to the current indicator application dimension. Based on the data type and precision of all indicators involved in the current indicator application dimension, automatically construct the columnar storage structure for the indicator result data corresponding to this application dimension, using the application dimension as the primary key and each indicator value as a regular key (data item).

[0082] Step S305: Row and column transformation of indicator result data for the same application dimension. Based on the columnar storage structure of the indicator result data corresponding to the current application dimension, use data table join query technology to perform a join query on the indicator result data storage table of all involved indicators, reorganize the data format into columns, and store it in the columnar storage table of the indicator result data corresponding to that application dimension.

[0083] Step S306: Locate the next record in the list of indicator application dimensions. Locate the next record in the correspondence list between the indicator application dimension and the list of indicator names in the same dimension (i.e., the next indicator application dimension after categorization and merging), and then proceed to step S302 to continue the judgment and processing.

[0084] Step S307: End. The indicator data storage format conversion and reconstruction are complete.

[0085] Optionally, the process for building data services for metrics across the same application dimension may include the following steps: Step S401: Locate the first record in the indicator application dimension list. Locate the first record in the correspondence table between the indicator application dimension and the list of indicator names in the same dimension. Starting from this record, for each categorized and merged indicator application dimension, automatically create an indicator data service that supports batch acquisition of multiple indicators.

[0086] Step S402: Determine if the entire application dimension list has been processed. Determine if all data in the indicator application dimension list has been processed. If yes, proceed to step S405; otherwise, proceed to step S403.

[0087] Step S403: Invoke the data lake indicator service management function to automatically create indicator data services. The exploration and development data lake indicator service management function is automatically invoked. Based on the columnar storage table of indicator result data corresponding to the current indicator application dimension, the function automatically configures and fills in relevant information such as service registration address, service name, request method, and return type, creating a multi-indicator data interface service corresponding to that application dimension.

[0088] Step S404: Publish the indicator data service. Register the generated indicator data interface service with the data lake's service management platform to publish the indicator data interface service.

[0089] Step S405: End. All indicator data services have been created.

[0090] like Figure 8 As shown in the figure, this embodiment proposes an oilfield financial indicator data push device, which may include: The first creation unit 801 is used to create an application dimension list based on the received oilfield business and financial indicator data. The application dimension list includes a one-to-one correspondence between multiple application dimensions and multiple groups of indicator names in the same dimension. The group of indicator names in the same dimension includes multiple first indicator names. The dimension reduction and aggregation unit 802 is used to perform dimension reduction and aggregation on the original data of the indicator results corresponding to each first indicator name, so as to obtain the dimension reduction and aggregation data corresponding to each first indicator name. The second creation unit 803 is used to create a corresponding columnar storage structure table for any group of indicator names in the same dimension. Storage unit 804 is used to store the dimension-reduced aggregated data corresponding to each first indicator name in the same dimension indicator name group into a columnar storage structure table. The push unit pushes the columnar storage structure table corresponding to each group of indicator names in the same dimension to the target system.

[0091] It should be noted that the processing procedures and beneficial effects of the first creation unit 801, the dimensionality reduction and aggregation unit 802, the second creation unit 803, the storage unit 804, and the push unit can be referred to respectively. Figure 1 Steps S101 to S105 are not described in detail here.

[0092] Optionally, the first creation unit 801 is also used for: An application request list is created based on oilfield financial indicators. The application request list includes multiple secondary indicator names and one-to-one correspondences between multiple application dimensions. For any second indicator name, obtain the business dimension corresponding to the second indicator name. If the application dimension corresponding to the second indicator name is a proper subset of the business dimension, then the second indicator name is used as a first indicator name. Each first indicator name corresponding to the same application dimension is treated as a group of indicator names of the same dimension, and the application dimension corresponding to the first indicator name in the group of indicator names of the same dimension is determined as the application dimension corresponding to the group of indicator names of the same dimension. Create an application dimension list based on each group of metric names within the same dimension and its corresponding application dimension.

[0093] Optionally, the original data of the indicator result corresponding to the first indicator name includes multiple rows of data, and each row of data includes the first indicator name, the dimension value under the business dimension, and the indicator value; The dimensionality reduction and aggregation unit 802 is also used for: For any first indicator name, determine the target dimension other than the first application dimension in the business dimension corresponding to the first indicator name. The first application dimension is the application dimension corresponding to the first indicator name. Find multiple rows of data to be aggregated in the original data of the indicator results corresponding to the first indicator name. The dimension values ​​under the first application dimension are the same in each row of data to be aggregated, but the dimension values ​​under the target dimension are different. Add or average the indicator values ​​in the multiple rows of data to be aggregated to obtain the aggregated indicator value. Aggregate the multiple rows of data to be aggregated into a new row of data and delete the target dimension and the corresponding dimension value to obtain the dimension-reduced aggregated data corresponding to the first indicator name. The new row of data includes the first indicator name, the dimension value under the first application dimension, and the aggregated indicator value.

[0094] Optionally, the second creation unit 803 is also used for: Based on the first application dimension corresponding to the first indicator name in the same dimension indicator name group, create a corresponding first column name, which includes the first application dimension; and based on each first indicator name in the same dimension indicator name group, create a corresponding second column name, which includes each first indicator name in the same dimension indicator name group. Based on the first and second column names, create a table header that includes the first and second column names, and create multiple blank rows of data with an expandable number of rows under the table header to obtain a columnar storage structure table corresponding to the same dimension indicator name group.

[0095] Optionally, storage unit 804 is also used for: For any first indicator name in the same dimension indicator name group, the target blank row data to be stored is determined in the columnar storage structure table. The dimension value under the first application dimension in the dimension-reduced aggregated data is stored in the position under the first column name in the target blank row data, and the indicator value in the dimension-reduced aggregated data is stored in the position under the second column name in the target blank row data.

[0096] Optionally, the above apparatus further includes a first processing unit for: After obtaining the business dimension corresponding to the second indicator name, if the application dimension corresponding to the second indicator name includes a dimension different from the business dimension, then the original data of the indicator result corresponding to the second indicator name is determined to be erroneous data, and the second indicator name is prohibited from being used as the first indicator name.

[0097] Optionally, the above-mentioned device further includes a second processing unit for: After obtaining the business dimension corresponding to the second indicator name, if the application dimension corresponding to the second indicator name is consistent with the business dimension corresponding to the second indicator name, then determine the same dimension indicator name group including the second indicator name, and store the original data of the indicator result corresponding to each second indicator name in the same dimension indicator name group into the corresponding columnar storage structure table.

[0098] The oilfield business and financial indicator data push device proposed in this embodiment can create an application dimension list based on the received oilfield business and financial indicator data. The application dimension list includes a one-to-one correspondence between multiple application dimensions and multiple groups of indicator names within the same dimension. Each group of indicator names within the same dimension includes multiple first indicator names. Dimensionality reduction and aggregation are performed on the original data corresponding to each first indicator name to obtain the dimensionality-reduced and aggregated data for each first indicator name. For any group of indicator names within the same dimension, a corresponding columnar storage structure table is created based on the group, and the dimensionality-reduced and aggregated data corresponding to each first indicator name in the same dimension group is stored in the columnar storage structure table. The columnar storage structure table corresponding to each group of indicator names is then pushed to the target system. This embodiment achieves the provision of business and financial indicator data to the target system by constructing columnar storage structure tables corresponding to groups of indicator names within the same dimension and pushing them to the target system, thereby reducing the occupation and consumption of network bandwidth resources.

[0099] In this embodiment, the oilfield financial indicator data push device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit), a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0100] This invention also provides a computer device having the above-described features. Figure 8 The device shown is for pushing data on oilfield financial indicators.

[0101] Please see Figure 9 The present invention provides a schematic diagram of the structure of a computer device according to an optional embodiment. The computer device includes one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, multiple processors and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 9 Take a processor 10 as an example.

[0102] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0103] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0104] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function. The data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0105] Memory 20 may include volatile memory, such as random access memory. Memory may also include non-volatile memory, such as flash memory, hard disk, or solid-state drive. Memory 20 may also include combinations of the above types of memory.

[0106] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0107] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An oilfield financial indicator data pushing method, characterized in that, The application comprises the following steps: According to the received oil field industry financial index data, an application dimension list is created, which includes a one-to-one correspondence between multiple application dimensions and multiple same dimension indicator name groups, and the same dimension indicator name group includes multiple first indicator names; The dimension reduction aggregation is performed on the indicator result original data corresponding to each first indicator name to obtain the dimension reduction aggregated data corresponding to each first indicator name; For any same dimension indicator name group, a corresponding columnar storage structure table is created according to the same dimension indicator name group, and the dimension reduction aggregated data corresponding to each first indicator name in the same dimension indicator name group is stored in the columnar storage structure table; The columnar storage structure table corresponding to each same dimension indicator name group is pushed to the target system.

2. The method of claim 1, wherein, The application comprises the following steps: According to the received oil field industry financial index data, an application dimension list is created, which includes a one-to-one correspondence between multiple application dimensions and multiple same dimension indicator name groups, and the same dimension indicator name group includes multiple first indicator names; For any same dimension indicator name group, a corresponding columnar storage structure table is created according to the same dimension indicator name group, and the dimension reduction aggregated data corresponding to each first indicator name in the same dimension indicator name group is stored in the columnar storage structure table; The columnar storage structure table corresponding to each same dimension indicator name group is pushed to the target system. The application comprises the following steps:

3. The method of claim 2, wherein, According to the received oil field industry financial index data, an application dimension list is created, which includes a one-to-one correspondence between multiple application dimensions and multiple same dimension indicator name groups, and the same dimension indicator name group includes multiple first indicator names; For any same dimension indicator name group, a corresponding columnar storage structure table is created according to the same dimension indicator name group, and the dimension reduction aggregated data corresponding to each first indicator name in the same dimension indicator name group is stored in the columnar storage structure table; The columnar storage structure table corresponding to each same dimension indicator name group is pushed to the target system.

4. The method of claim 3, wherein, The application comprises the following steps: According to the received oil field industry financial index data, an application dimension list is created, which includes a one-to-one correspondence between multiple application dimensions and multiple same dimension indicator name groups, and the same dimension indicator name group includes multiple first indicator names; For any same dimension indicator name group, a corresponding columnar storage structure table is created according to the same dimension indicator name group, and the dimension reduction aggregated data corresponding to each first indicator name in the same dimension indicator name group is stored in the columnar storage structure table; The columnar storage structure table corresponding to each same dimension indicator name group is pushed to the target system. The application comprises the following steps: According to the received oil field industry financial index data, an application dimension list is created, which includes a one-to-one correspondence between multiple application dimensions and multiple same dimension indicator name groups, and the same dimension indicator name group includes multiple first indicator names; For any same dimension indicator name group, a corresponding columnar storage structure table is created according to the same dimension indicator name group, and the dimension reduction aggregated data corresponding to each first indicator name in the same dimension indicator name group is stored in the columnar storage structure table; The columnar storage structure table corresponding to each same dimension indicator name group is pushed to the target system. The application comprises the following steps: According to the received oil field industry financial index data, an application dimension list is created, which includes a one-to-one correspondence between multiple application dimensions and multiple same dimension indicator name groups, and the same dimension indicator name group includes multiple first indicator names; For any same dimension indicator name group, a corresponding columnar storage structure table is created according to the same dimension indicator name group, and the dimension reduction aggregated data corresponding to each first indicator name in the same dimension indicator name group is stored in the columnar storage structure table; The columnar storage structure table corresponding to each same dimension indicator name group is pushed to the target system. create a corresponding first column name according to the first application dimension corresponding to the first index name in the same-dimension index name group, the first column name including the first application dimension; and create a corresponding second column name according to each first index name in the same-dimension index name group, the second column name including each first index name in the same-dimension index name group; create a table header including the first column name and the second column name according to the first column name and the second column name, and create a plurality of blank row data with extensible row numbers under the table header, to obtain a columnar storage structure table corresponding to the same-dimension index name group.

5. The method of claim 4, wherein, The storing of the dimension-reduced and aggregated data corresponding to each first index name in the same-dimension index name group into the columnar storage structure table includes: For the dimension-reduced and aggregated data corresponding to any first index name in the same-dimension index name group, determining target blank row data to be stored in the columnar storage structure table, storing the dimension value under the first application dimension in the dimension-reduced and aggregated data into a position under the first column name in the target blank row data, and storing the index value in the dimension-reduced and aggregated data into a position under the second column name in the target blank row data.

6. The method of claim 2, wherein, After the obtaining of the business dimension corresponding to the second index name, the method further includes: If the application dimension corresponding to the second index name includes a dimension different from the business dimension, determining that the index result original data corresponding to the second index name is error data, and prohibiting the second index name from being used as the first index name.

7. The method of claim 2, wherein, After the obtaining of the business dimension corresponding to the second index name, the method further includes: If the application dimension corresponding to the second index name is consistent with the business dimension corresponding to the second index name, determining the same-dimension index name group including the second index name, and storing the index result original data corresponding to each second index name in the same-dimension index name group into a corresponding columnar storage structure table.

8. An oilfield industry financial indicator data pushing device, characterized in that, including: a first creating unit configured to create an application dimension list according to received oilfield industry and finance index data, the application dimension list including a one-to-one correspondence relationship between a plurality of application dimensions and a plurality of same-dimension index name groups, and the same-dimension index name group including a plurality of first index names; a dimension-reducing and aggregating unit configured to respectively perform dimension reduction and aggregation on index result original data corresponding to each first index name, to obtain dimension-reduced and aggregated data corresponding to each first index name; a second creating unit configured to, for any same-dimension index name group, create a corresponding columnar storage structure table according to the same-dimension index name group; a storing unit configured to store the dimension-reduced and aggregated data corresponding to each first index name in the same-dimension index name group into the columnar storage structure table; a pushing unit configured to push the columnar storage structure table corresponding to each same-dimension index name group to a target system.

9. A computer device, comprising: including: A memory and a processor are connected in communication with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the oilfield financial index data pushing method in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing a computer to perform the oilfield financial index data pushing method in any one of claims 1 to 7.

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