Data processing method and device for bank agent business, electronic equipment and medium

Abstract

This application provides a data processing method, apparatus, electronic device, and medium for bank agency sales business, belonging to the field of data processing technology. The method includes: extracting a first basic parameter and a second basic parameter from the acquired transaction details and share details, respectively; performing logical condition matching between the first basic parameter and the second basic parameter and a ledger rule table, respectively; if the matching is successful, splitting the transaction details and share details into data segments, extracting a first detailed parameter and a second detailed parameter from the split sales ledger data and the overall asset balance data, respectively, and performing logical condition matching between the first detailed parameter and the second detailed parameter and the ledger rule table, respectively; if the matching is successful, extracting the target parameter row data that has been matched and performing calculations to obtain the final assessment element data. The provided data processing method for bank agency sales business can automatically split and calculate the assessment elements for each branch, improving business execution efficiency.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data processing method, apparatus, electronic device and medium for bank agency sales business. Background Technology

[0002] The bank's agency sales business has continued to grow. Under the overall coordination of the head office, resources from products, channels, and customers have been integrated, resulting in substantial growth in agency sales volume. Currently, the bank has various cooperation methods with channels and product providers, such as mutual traffic generation, combined investment, and resource exchange. Each type of cooperation involves signing agency sales cooperation agreements with multiple partner institutions and requires the participation of several branches. Therefore, in the final performance distribution, it is necessary to continuously allocate sales volume, balance, revenue, and other assessment factors to the corresponding branches according to the agreed proportions or distribution coefficients over a period of time, based on the stipulations in the cooperation agreements.

[0003] In this type of business model, the logic and conditions of the branch-level performance evaluation rules become more complex, involving more calculation steps and configuring more calculation coefficients. Furthermore, different rules have different effective time intervals (each cooperation agreement specifies a separate cooperation period). The original rules, which only configured calculation coefficients based on product difficulty and whether a customer was new, and whose parameters were simply uniformly used for sales conversion in the ledger generation model, can no longer meet the needs of business development. Relying solely on manual ledgers is also insufficient to handle the situation of dozens of sales agreements that are constantly increasing, requiring the processing of tens of thousands of data entries daily. Therefore, how to calculate the performance evaluation elements for each branch during the bank's sales agency business processing has become a pressing issue that needs to be addressed. Summary of the Invention

[0004] To address the aforementioned technical problems, embodiments of this application provide a data processing method, apparatus, electronic device, and computer-readable storage medium for bank agency sales business.

[0005] In a first aspect, embodiments of this application provide a data processing method for bank agency sales business, the method comprising: obtaining transaction details and share details of bank agency sales business from a data source;

[0006] Extract the first basic parameters corresponding to multiple element fields from the transaction details;

[0007] Extract the second basic parameters corresponding to multiple element fields from the share details;

[0008] Perform logical condition matching between the first basic parameter and each parameter row of the ledger rule table;

[0009] If a match is successful, the transaction details will be split to obtain sales ledger data;

[0010] Perform logical condition matching between the second basic parameter and each parameter row of the ledger rule table;

[0011] If a match is successful, the share details will be split to obtain the overall asset balance data;

[0012] Extract the first detailed parameters corresponding to multiple element fields from the sales ledger data;

[0013] Extract the second detailed parameters corresponding to multiple element fields from the overall asset balance data;

[0014] The first detailed parameter and the second detailed parameter are respectively matched with the parameter rows of the ledger rule table using logical conditions.

[0015] If a match is successful, the target parameter row data that was successfully matched is extracted and calculated to obtain the final assessment element data.

[0016] In one embodiment, the ledger rule table includes: a required parameter table; the logical condition includes: a first logical condition, which includes: an effective time period and a product difficulty coefficient;

[0017] The step of performing logical condition matching between the first basic parameter and each parameter row of the ledger rule table includes:

[0018] When the first basic parameter matches the effective time period and the product difficulty coefficient, the logical match is determined to be successful.

[0019] In one embodiment, the ledger rule table includes: a first non-essential parameter table; the logical condition includes: a second logical condition, the second logical condition including: the effective time period and the first parameter condition;

[0020] The step of performing logical condition matching between the first basic parameter and each parameter row of the ledger rule table includes:

[0021] When the first basic parameter meets the effective time period and the first parameter condition, the matching is determined to be successful;

[0022] The step of performing logical condition matching between the second basic parameter and each parameter row of the ledger rule table includes:

[0023] When the second basic parameter meets the effective time period and the first parameter condition, the match is determined to be successful.

[0024] In one embodiment, the method further includes: the ledger rule table includes: a second non-essential parameter table; the logical condition includes: a third logical condition, the third logical condition including: the effective time period and the second parameter condition;

[0025] The step of performing logical condition matching between the second basic parameter and each parameter row of the ledger rule table includes:

[0026] When the second basic parameter meets the effective time period and the second parameter condition, the match is determined to be successful.

[0027] In one embodiment, splitting the transaction details to obtain sales ledger data includes:

[0028] When the first basic parameter meets the effective time period and the product difficulty coefficient, the corresponding product sales coefficient and transaction sales coefficient are extracted through the required parameter table.

[0029] When the first basic parameter meets the effective time period and the first parameter condition, the first non-essential parameter table splits the transaction detail data according to the partial adjustment coefficient of the transaction details, extracts the partial adjustment coefficient of the transaction details, and obtains the first partial adjustment coefficient.

[0030] The sales ledger data is obtained by multiplying the transaction details data by the product sales coefficient, the transaction sales coefficient, and the first division adjustment coefficient.

[0031] The step of splitting the share details to obtain the overall asset balance data includes:

[0032] When the second basic parameter meets the effective time period and the second parameter condition, the corresponding overall asset balance coefficient is extracted from the second non-essential parameter table;

[0033] When the second basic parameter meets the effective time period and the first parameter condition, the first non-essential parameter table splits the share detail data according to the share detail's partial adjustment coefficient, extracts the share detail's partial adjustment coefficient, and obtains the second partial adjustment coefficient.

[0034] The total asset balance data is obtained by multiplying the share details data by the total asset balance coefficient and the second segment adjustment coefficient.

[0035] In one implementation, the final assessment data includes: revenue data, sales volume score data, and total asset balance score data;

[0036] The extracted and successfully matched target parameter rows are used for calculation to obtain the final assessment element data, including:

[0037] The first calculated coefficients corresponding to the target parameter row are extracted using the required parameter table, and the second calculated coefficients corresponding to the target parameter row are extracted using the second non-required parameter table.

[0038] Based on the first calculation coefficient, the second calculation coefficient, and the target parameter row data, revenue data, sales points data, and total asset balance points data are obtained.

[0039] In one embodiment, the method further includes:

[0040] If the first detailed parameter fails to logically match with each parameter row of the ledger rule table, or if the second detailed parameter fails to logically match with each parameter row of the ledger rule table, then the default parameter row data is extracted for calculation to ensure the successful execution of the assessment data.

[0041] Secondly, embodiments of this application provide a data processing apparatus for bank agency sales business, the data processing apparatus for bank agency sales business comprising:

[0042] The first extraction module is used to extract first basic parameters corresponding to multiple element fields from the transaction details, and at the same time, it is used to extract second basic parameters corresponding to multiple element fields from the share details.

[0043] The second extraction module is used to extract the first detailed parameters corresponding to multiple element fields from the sales ledger data, and to extract the second detailed parameters corresponding to multiple element fields from the total asset balance data.

[0044] The first matching module is used to perform logical condition matching between the first basic parameter and each parameter row of the ledger rule table, and to perform logical condition matching between the second basic parameter and each parameter row of the ledger rule table.

[0045] The second matching module is used to perform logical condition matching between the first detailed parameter and the second detailed parameter and each parameter row of the ledger rule table, respectively.

[0046] The calculation module is used to calculate the target parameter rows that have been successfully matched to obtain the final assessment element data.

[0047] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the computer program executes the data processing method for bank agency business provided in the first aspect when the processor is running.

[0048] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when run on a processor, executes the data processing method for bank agency sales provided in the first aspect.

[0049] The data processing method, apparatus, electronic device, and medium for bank agency sales business provided in this application obtain transaction details and share details of bank agency sales business from a data source; extract first basic parameters corresponding to multiple element fields from the transaction details; extract second basic parameters corresponding to multiple element fields from the share details; perform logical condition matching between the first basic parameters and each parameter row of the ledger rule table; if the matching is successful, split the transaction details to obtain sales ledger data; perform logical condition matching between the second basic parameters and each parameter row of the ledger rule table; if the matching is successful, split the share details to obtain overall asset balance data; extract first detailed parameters corresponding to multiple element fields from the sales ledger data; extract second detailed parameters corresponding to multiple element fields from the overall asset balance data; perform logical condition matching between the first detailed parameters and the second detailed parameters respectively and each parameter row of the ledger rule table; if the matching is successful, extract the target parameter row data that has been matched successfully for calculation to obtain the final assessment element data. By automating the calculation of assessment element data, the efficiency of bank agency sales business execution is improved. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be considered as a limitation on the scope of protection of this application. In the various drawings, similar components are numbered similarly.

[0051] Figure 1 A flowchart illustrating a data processing method for bank agency sales business provided in an embodiment of this application is shown.

[0052] Figure 2 This paper illustrates another flowchart of the data processing method for bank sales agency business provided in an embodiment of this application;

[0053] Figure 3 This paper illustrates another flowchart of the data processing method for bank sales agency business provided in an embodiment of this application;

[0054] Figure 4 This paper shows a schematic diagram of a partial adjustment coefficient configuration provided in an embodiment of this application.

[0055] Figure 5 This paper illustrates another flowchart of the data processing method for bank sales agency business provided in an embodiment of this application;

[0056] Figure 6 A schematic diagram of the structure of a data processing device for bank agency sales provided in an embodiment of this application is shown;

[0057] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0058] Icons: 600 - Data processing device for bank agency sales business, 601 - First extraction module, 602 - Second extraction module, 603 - First matching module, 604 - Second matching module, 605 - Calculation module, 700 - Electronic device, 701 - Transceiver, 702 - Processor, 703 - Memory. Detailed Implementation

[0059] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0060] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0061] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0062] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0063] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0064] Example 1

[0065] This application provides a data processing method for bank agency sales business. This data processing method for bank agency sales business can be applied to electronic devices, which can be business processing devices of financial institutions such as banks and securities firms.

[0066] See Figure 1 Data processing methods for bank agency sales business include:

[0067] Step S101: Obtain transaction details and share details of the bank's agency sales business from the data source.

[0068] In this embodiment, daily transaction details and share details of the bank's agency sales business are obtained from the data source of the bank's transaction system. The transaction details data are the daily transaction flow data of the bank, and the share details data are the share data of the account balance.

[0069] Step S102: Extract the first basic parameters corresponding to multiple element fields from the transaction details.

[0070] In this embodiment, nine element fields are extracted from the transaction details: product code, product identifier, product manager, customer code, customer name, customer type, account code, account type, and account identifier. The first basic parameter values ​​are the effective time period and product difficulty coefficient extracted from the transaction details.

[0071] To further clarify, the effective period refers to the time frame during which transaction details are processed, generally the business day on which the transaction takes place. The product difficulty coefficient is a manually configured factor based on the difficulty of the transaction.

[0072] Step S103: Extract the second basic parameters corresponding to multiple element fields from the share details.

[0073] In this embodiment, nine element fields are extracted from the share details: product code, product identifier, product manager, customer code, customer name, customer type, account code, account type, and account identifier. The second basic parameter is the basic parameter value of the effective time period and optional parameter conditions extracted from the share details.

[0074] Step S104: Perform logical condition matching between the first basic parameter and each parameter row of the ledger rule table.

[0075] In this embodiment, the first basic parameter is matched against the required parameter table, the first non-required parameter table, and the second non-required parameter table of the ledger rule table. If the first basic parameter cannot be matched against the required parameter table, no data can be generated. If the first basic parameter cannot be matched against the non-required parameter table, a default parameter row is selected to generate data. The default parameter row contains default values ​​preset by the system.

[0076] Step S105: If the matching is successful, the transaction details are split to obtain sales ledger data.

[0077] In this embodiment, if the logical conditions of the first basic parameter match those of the required parameter table, the first non-required parameter table, and the second non-required parameter table in the ledger rule table, then the product sales volume coefficient and transaction sales volume coefficient from the required parameter table and the distribution adjustment coefficient from the first non-required parameter table are taken. The original sales volume in the transaction data is multiplied by the product sales volume coefficient, the transaction sales volume coefficient, and the distribution adjustment coefficient to obtain the sales ledger data.

[0078] In one embodiment, the ledger rule table includes: a first non-essential parameter table; the logical conditions include: a second logical condition, the second logical condition including: an effective time period and a first parameter condition; the step of matching the first basic parameter with each parameter row of the ledger rule table includes: when the first basic parameter meets the effective time period and the first parameter condition, the matching is determined to be successful; the step of matching the second basic parameter with each parameter row of the ledger rule table includes: when the second basic parameter meets the effective time period and the first parameter condition, the matching is determined to be successful.

[0079] Please see Figure 2 Step S105 includes:

[0080] Step S1051: When the first basic parameter meets the effective time period and the product difficulty coefficient, the corresponding product sales coefficient and transaction sales coefficient are extracted through the required parameter table.

[0081] Step S1052: When the first basic parameter meets the effective time period and the first parameter condition, the first non-essential parameter table splits the transaction detail data according to the partial adjustment coefficient of the transaction details, extracts the partial adjustment coefficient of the transaction details, and obtains the first partial adjustment coefficient.

[0082] In this embodiment, the transaction details data are split into parts according to the agreed-upon part adjustment coefficient, and the transaction data is distributed to each branch according to the part adjustment coefficient.

[0083] Step S1053: Multiply the transaction details data by the product sales coefficient, the transaction sales coefficient, and the first division adjustment coefficient to obtain the sales ledger data.

[0084] The system extracts the original sales data from the transaction details data, multiplies the original sales data by the product sales coefficient and transaction sales coefficient extracted from the required parameter table and the distribution adjustment coefficient extracted from the first non-required parameter table, and allocates it to the corresponding sales ledger data of the relevant branch.

[0085] Step S106: Perform logical condition matching between the second basic parameter and each parameter row of the ledger rule table.

[0086] In this embodiment, the second basic parameter is matched against the required parameter table, the first non-required parameter table, and the second non-required parameter table of the ledger rule table. If the second basic parameter cannot be matched against the required parameter table, no data can be generated. If the second basic parameter cannot be matched against the non-required parameter table, a default parameter row is selected to generate data. The default parameter row contains default values ​​preset by the system.

[0087] Step S107: If the matching is successful, the share details are split into data to obtain the total asset balance data.

[0088] In this embodiment, if the second basic parameter matches the logical conditions of the required parameter table, the first non-required parameter table, and the second non-required parameter table in the ledger rule table, then the sub-adjustment coefficient of the first non-required parameter table and the overall asset balance coefficient of the second non-required parameter table are extracted, and the share detail data is multiplied by the overall asset balance coefficient and the second sub-adjustment coefficient to obtain the overall asset balance data.

[0089] Furthermore, if there are matching parameter rows, the data is split according to the distribution adjustment coefficient of the row with the earliest creation time.

[0090] In one embodiment, the ledger rule table includes: a second non-essential parameter table; the logical conditions include: a third logical condition, the third logical condition including: an effective time period and a second parameter condition; the step of matching the second basic parameter with each parameter row of the ledger rule table includes: when the second basic parameter meets the effective time period and the second parameter condition, the matching is determined to be successful.

[0091] Please see Figure 3 Step S107 includes:

[0092] Step S1071: When the second basic parameter meets the effective time period and the second parameter condition, the corresponding overall asset balance coefficient is extracted from the second non-essential parameter table.

[0093] In this embodiment, the second parameter condition can select up to two parameter conditions, and each parameter condition can select nine parameter types, namely product code / name, product identifier, product manager, customer code, customer name, customer type, account code / number, account type, account identifier, and logical matching supports two types (belongs to, does not belong to). For details on modifying parameters, please refer to the page. Figure 4 As shown.

[0094] Step S1072: When the second basic parameter meets the effective time period and the first parameter condition, the first non-essential parameter table splits the share detail data according to the share detail partial adjustment coefficient, extracts the share detail partial adjustment coefficient, and obtains the second partial adjustment coefficient.

[0095] In this embodiment, the partial adjustment coefficient can be configured with a maximum of 25 values. The extracted partial adjustment coefficients are manually configured according to a pre-agreed standard. A schematic diagram of the specific partial adjustment coefficient configuration is shown below. Figure 4 As shown.

[0096] Step S1073: Multiply the share details data by the overall asset balance coefficient and the second segment adjustment coefficient to obtain the overall asset balance data.

[0097] Specifically, the system multiplies the overall asset balance coefficient extracted from the second non-essential parameter table with the segment adjustment coefficient extracted from the first non-essential parameter table, and each branch obtains the corresponding overall asset balance data.

[0098] Step S108: Extract the first detailed parameters corresponding to multiple element fields from the sales ledger data.

[0099] In this embodiment, nine element fields are extracted from the sales ledger data: product code, product identifier, product manager, customer code, customer name, customer type, account code, account type, and account identifier. The first detailed parameter is the effective time period and product difficulty coefficient extracted from the sales ledger data.

[0100] Step S109: Extract the second detailed parameters corresponding to multiple element fields from the overall asset balance data.

[0101] In this embodiment, nine element fields are extracted from the share details, namely product code, product identifier, product manager, customer code, customer name, customer type, account code, account type, and account identifier. The second detail parameter is the detailed parameter value of the effective time period and optional parameter conditions extracted from the overall asset balance data.

[0102] Step S110: Logically match the first detailed parameter and the second detailed parameter with each parameter row of the ledger rule table.

[0103] In this embodiment, the first detailed data and the second detailed data are matched simultaneously with the parameter rows of the required parameter table, the first non-required parameter table and the second non-required parameter table.

[0104] Step S111: If the matching is successful, extract the target parameter row data that has been matched and perform calculations to obtain the final assessment element data.

[0105] In one implementation, if the first detailed parameter fails to logically match with each parameter row of the ledger rule table, or if the second detailed parameter fails to logically match with each parameter row of the ledger rule table, then the default parameter row data is extracted for calculation to ensure the successful execution of the assessment data.

[0106] Furthermore, if a match is successful, the calculation coefficient of the earliest created record (the highest priority by system default) is taken, and the revenue, sales points, and total asset points are calculated in sequence.

[0107] like Figure 5 As shown, step S111 includes:

[0108] Step S1111: Extract the first calculation coefficient corresponding to the target parameter row through the required parameter table, and extract the second calculation coefficient corresponding to the target parameter row through the second non-required parameter table.

[0109] In this embodiment, the original sales volume per 100 million and the total assets at the end of each quarter per 100 million are extracted through the required parameter table, the corresponding segment adjustment coefficient is extracted through the first non-required parameter table, and the corresponding trailing commission coefficient, revenue distribution coefficient, sales points distribution coefficient and total asset points distribution coefficient are extracted through the second non-required parameter table.

[0110] Step S1112: Based on the first calculation coefficient, the second calculation coefficient, and the target parameter row data, obtain the revenue data, sales points data, and total asset balance points data.

[0111] In this embodiment, the final assessment data includes: revenue data, sales volume points data, and total asset balance points data. The first calculation coefficient is the original sales volume per 100 million and the total assets at the end of the quarter per 100 million. The second calculation coefficient is the trailing commission coefficient, revenue distribution coefficient, sales volume points distribution coefficient, and total asset points distribution coefficient.

[0112] Specifically, sales points are calculated by multiplying the original sales volume per 100 million by the original sales volume and the sales points distribution coefficient. Total asset balance points are calculated by multiplying the total asset balance by the total assets per 100 million at the end of the quarter and the total asset points distribution coefficient. Revenue is calculated by multiplying the total asset balance by (management fee rate multiplied by the trailing commission coefficient plus the sales service fee rate) divided by 365 and then multiplying by the revenue distribution coefficient. The management fee rate and sales service fee rate are both automatically generated by the system.

[0113] Furthermore, the system includes an application rules button. Since two additional non-essential parameter tables have been added, each table will have over 100 parameters configured according to current business practices. Therefore, the system provides a unified button to trigger calculations. Clicking this button will perform full ledger calculations in real time according to the latest rule parameter tables. A screenshot of the first non-essential parameter table page is shown in Table 1, and a screenshot of the second non-essential parameter table page is shown in Table 2.

[0114] Table 1:

[0115]

[0116] Table 2:

[0117]

[0118] The data processing method for bank agency sales provided in this embodiment, as described in this application, obtains transaction details and share details of the bank agency sales business from a data source; extracts first basic parameters corresponding to multiple element fields from the transaction details; extracts second basic parameters corresponding to multiple element fields from the share details; performs logical condition matching between the first basic parameters and each parameter row of the ledger rule table; if the matching is successful, the transaction details are split into sales ledger data; the second basic parameters are logically matched with each parameter row of the ledger rule table; if the matching is successful, the share details are split into overall asset balance data; extracts first detailed parameters corresponding to multiple element fields from the sales ledger data; extracts second detailed parameters corresponding to multiple element fields from the overall asset balance data; performs logical condition matching between the first detailed parameters and the second detailed parameters respectively with each parameter row of the ledger rule table; if the matching is successful, the target parameter row data that has been successfully matched is extracted and calculated to obtain the final assessment element data. By automating the calculation of assessment element data, the efficiency of bank agency sales business execution is improved.

[0119] Example 2

[0120] Furthermore, embodiments of this application provide a data processing device for bank agency sales business, which is applied to electronic devices.

[0121] like Figure 6As shown, the data processing device 600 for bank agency sales includes:

[0122] The first extraction module 601 is used to extract first basic parameters corresponding to multiple element fields from the transaction details, and at the same time, it is used to extract second basic parameters corresponding to multiple element fields from the share details.

[0123] The second extraction module 602 is used to extract first detailed parameters corresponding to multiple element fields from the sales ledger data and extract second detailed parameters corresponding to multiple element fields from the overall asset balance data.

[0124] The first matching module 603 is used to perform logical condition matching between the first basic parameter and each parameter row of the ledger rule table, and to perform logical condition matching between the second basic parameter and each parameter row of the ledger rule table.

[0125] The second matching module 604 is used to perform logical condition matching between the first detailed parameter and the second detailed parameter and each parameter row of the ledger rule table, respectively.

[0126] The calculation module 605 is used to calculate the target parameter row data that has been successfully matched to obtain the final assessment element data.

[0127] The data processing device 600 for bank agency sales provided in this embodiment can realize the data processing of bank agency sales provided in Embodiment 1. To avoid repetition, it will not be described again here.

[0128] The data processing device for bank agency sales business provided in this embodiment obtains transaction details and share details of bank agency sales business from a data source; extracts first basic parameters corresponding to multiple element fields from the transaction details; extracts second basic parameters corresponding to multiple element fields from the share details; performs logical condition matching between the first basic parameters and each parameter row of the ledger rule table; if the matching is successful, the transaction details are split into sales ledger data; the second basic parameters are logically matched with each parameter row of the ledger rule table; if the matching is successful, the share details are split into overall asset balance data; extracts first detailed parameters corresponding to multiple element fields from the sales ledger data; extracts second detailed parameters corresponding to multiple element fields from the overall asset balance data; performs logical condition matching between the first detailed parameters and the second detailed parameters respectively with each parameter row of the ledger rule table; if the matching is successful, the target parameter row data that has been successfully matched is extracted and calculated to obtain the final assessment element data. By automating the calculation of assessment element data, the efficiency of bank agency sales business execution is improved.

[0129] Example 3

[0130] Furthermore, this application provides an electronic device, including a memory and a processor. The memory stores a computer program, which, when run on the processor, executes the data processing method for bank sales agency business provided in Embodiment 1.

[0131] For details, see Figure 7 The electronic device 700 includes a transceiver 701, a bus interface, and a processor 702. The processor 702 is used for: obtaining transaction details and share details of bank agency sales business from a data source; extracting first basic parameters corresponding to multiple element fields from the transaction details; extracting second basic parameters corresponding to multiple element fields from the share details; performing logical condition matching between the first basic parameters and each parameter row of the ledger rule table; if the matching is successful, splitting the transaction details to obtain sales ledger data; performing logical condition matching between the second basic parameters and each parameter row of the ledger rule table; if the matching is successful, splitting the share details to obtain overall asset balance data; extracting first detailed parameters corresponding to multiple element fields from the sales ledger data; extracting second detailed parameters corresponding to multiple element fields from the overall asset balance data; performing logical condition matching between the first detailed parameters and the second detailed parameters respectively and each parameter row of the ledger rule table; if the matching is successful, extracting the target parameter row data that has been matched successfully for calculation to obtain the final assessment element data.

[0132] In one embodiment, the processor 702 is further configured to: the ledger rule table includes: a required parameter table; the logical condition includes: a first logical condition, the first logical condition including: an effective time period and a product difficulty coefficient; the step of matching the first basic parameter with each parameter row of the ledger rule table includes: when the first basic parameter matches the effective time period and the product difficulty coefficient, then the logical match is determined to be successful.

[0133] In one embodiment, the processor 702 is further configured to: the ledger rule table includes: a first non-essential parameter table; the logical condition includes: a second logical condition, the second logical condition including: an effective time period and a first parameter condition; the step of matching the first basic parameter with each parameter row of the ledger rule table includes: when the first basic parameter meets the effective time period and the first parameter condition, then a successful match is determined; the step of matching the second basic parameter with each parameter row of the ledger rule table includes: when the second basic parameter meets the effective time period and the first parameter condition, then a successful match is determined.

[0134] In one embodiment, the processor 702 is further configured to: the ledger rule table includes: a second non-essential parameter table; the logical condition includes: a third logical condition, the third logical condition including: an effective time period and a second parameter condition; the step of matching the second basic parameter with each parameter row of the ledger rule table includes: when the second basic parameter meets the effective time period and the second parameter condition, then the matching is determined to be successful.

[0135] In one embodiment, the processor 702 is further configured to: when the first basic parameter meets the effective time period and the product difficulty coefficient, extract the corresponding product sales coefficient and transaction sales coefficient through the required parameter table; when the first basic parameter meets the effective time period and the first parameter condition, split the transaction detail data according to the partial adjustment coefficient of the transaction details in the first non-required parameter table, extract the partial adjustment coefficient of the transaction details, and obtain the first partial adjustment coefficient; multiply the transaction detail data with the product sales coefficient, the transaction sales coefficient, and the first partial adjustment coefficient to obtain the sales ledger data; The step of splitting the share details to obtain the overall asset balance data includes: when the second basic parameter meets the effective time period and the second parameter condition, the second non-essential parameter table extracts the corresponding overall asset balance coefficient; when the second basic parameter meets the effective time period and the first parameter condition, the first non-essential parameter table splits the share detail data according to the segment adjustment coefficient of the share details, extracts the segment adjustment coefficient of the share details, and obtains the second segment adjustment coefficient; the share detail data is multiplied by the overall asset balance coefficient and the second segment adjustment coefficient to obtain the overall asset balance data.

[0136] In one embodiment, the processor 702 is further configured to: include the final assessment element data as follows: revenue data, sales points data, and total asset balance points data; the step of extracting the successfully matched target parameter row data for calculation to obtain the final assessment element data includes: extracting the first calculation coefficient corresponding to the target parameter row through a required parameter table, and extracting the second calculation coefficient corresponding to the target parameter row through a second non-required parameter table; and obtaining the revenue data, sales points data, and total asset balance points data based on the first calculation coefficient, the second calculation coefficient, and the target parameter row data.

[0137] In one embodiment, the processor 702 is further configured to: if the first detailed parameter fails to logically match with each parameter row of the ledger rule table, or if the second detailed parameter fails to logically match with each parameter row of the ledger rule table, extract the default parameter row data for calculation to ensure the successful execution of the assessment data.

[0138] In this embodiment of the application, the electronic device 700 further includes a memory 703. Figure 7 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 702) and memory (memory 703). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 701 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. The processor 702 is responsible for managing the bus architecture and general processing, and the memory 703 can store data used by the processor 702 during operation.

[0139] The electronic device 700 provided in this application embodiment can execute the steps of the data processing method for bank sales business provided in the above method embodiment 1. To avoid repetition, it will not be described again here.

[0140] The electronic device provided in this embodiment obtains transaction details and share details of bank agency sales business from a data source; extracts first basic parameters corresponding to multiple element fields from the transaction details; extracts second basic parameters corresponding to multiple element fields from the share details; performs logical condition matching between the first basic parameters and each parameter row of the ledger rule table; if the matching is successful, the transaction details are split into sales ledger data; the second basic parameters are logically matched with each parameter row of the ledger rule table; if the matching is successful, the share details are split into overall asset balance data; extracts first detailed parameters corresponding to multiple element fields from the sales ledger data; extracts second detailed parameters corresponding to multiple element fields from the overall asset balance data; performs logical condition matching between the first detailed parameters and the second detailed parameters respectively with each parameter row of the ledger rule table; if the matching is successful, the target parameter row data that has been successfully matched is extracted and calculated to obtain the final assessment element data. By automating the calculation of assessment element data, the efficiency of bank agency sales business execution is improved.

[0141] Example 4

[0142] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the data processing method for bank agency sales provided in Embodiment 1.

[0143] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0144] The computer-readable storage medium provided in this embodiment can implement the data processing method for bank sales agency business provided in Embodiment 1. To avoid repetition, it will not be described again here.

[0145] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0146] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0147] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A data processing method for bank agency sales business, characterized in that, The method includes: Obtain transaction details and share details of the bank's agency sales business from the data source; Extract the first basic parameters corresponding to multiple element fields from the transaction details; Extract the second basic parameters corresponding to multiple element fields from the share details; The first basic parameter is logically matched with each parameter row of the ledger rule table; the ledger rule table includes: a required parameter table, a first non-required parameter table, and a second non-required parameter table; the logical conditions include: a first logical condition, a second logical condition, and a third logical condition, wherein the first logical condition includes: an effective time period and a product difficulty coefficient; the second logical condition includes: the effective time period and the first parameter condition; and the third logical condition includes: the effective time period and the second parameter condition. If a match is successful, the transaction details will be split to obtain sales ledger data; Perform logical condition matching between the second basic parameter and each parameter row of the ledger rule table; If a match is successful, the share details will be split to obtain the overall asset balance data; Extract the first detailed parameters corresponding to multiple element fields from the sales ledger data; Extract the second detailed parameters corresponding to multiple element fields from the overall asset balance data; The first detailed parameter and the second detailed parameter are respectively matched with the parameter rows of the ledger rule table using logical conditions. If a match is successful, the target parameter row data that was successfully matched is extracted and calculated to obtain the final assessment element data; The step of splitting the transaction details to obtain sales ledger data includes: When the first basic parameter meets the effective time period and the product difficulty coefficient, the corresponding product sales coefficient and transaction sales coefficient are extracted through the required parameter table. When the first basic parameter meets the effective time period and the first parameter condition, the first non-essential parameter table splits the transaction detail data according to the partial adjustment coefficient of the transaction details, extracts the partial adjustment coefficient of the transaction details, and obtains the first partial adjustment coefficient. The sales ledger data is obtained by multiplying the transaction details data by the product sales coefficient, the transaction sales coefficient, and the first division adjustment coefficient. The step of splitting the share details to obtain the overall asset balance data includes: When the second basic parameter meets the effective time period and the second parameter condition, the corresponding overall asset balance coefficient is extracted from the second non-essential parameter table; When the second basic parameter meets the effective time period and the first parameter condition, the first non-essential parameter table splits the share detail data according to the share detail's partial adjustment coefficient, extracts the share detail's partial adjustment coefficient, and obtains the second partial adjustment coefficient. The total asset balance data is obtained by multiplying the share details data by the total asset balance coefficient and the second segment adjustment coefficient.

2. The method according to claim 1, characterized in that, The step of performing logical condition matching between the first basic parameter and each parameter row of the ledger rule table includes: When the first basic parameter matches the effective time period and the product difficulty coefficient, the logical match is determined to be successful.

3. The method according to claim 2, characterized in that, The step of performing logical condition matching between the first basic parameter and each parameter row of the ledger rule table includes: When the first basic parameter meets the effective time period and the first parameter condition, the matching is determined to be successful; The step of performing logical condition matching between the second basic parameter and each parameter row of the ledger rule table includes: When the second basic parameter meets the effective time period and the first parameter condition, the match is determined to be successful.

4. The method according to claim 2, characterized in that, include: The step of performing logical condition matching between the second basic parameter and each parameter row of the ledger rule table includes: When the second basic parameter meets the effective time period and the second parameter condition, the match is determined to be successful.

5. The method according to claim 1, characterized in that, The final assessment data includes: revenue data, sales volume score data, and total asset balance score data; The extracted and successfully matched target parameter rows are used for calculation to obtain the final assessment element data, including: The first calculated coefficients corresponding to the target parameter row are extracted using the required parameter table, and the second calculated coefficients corresponding to the target parameter row are extracted using the second non-required parameter table. Based on the first calculation coefficient, the second calculation coefficient, and the target parameter row data, revenue data, sales points data, and total asset balance points data are obtained.

6. The method according to claim 5, characterized in that, The method further includes: If the first detailed parameter fails to logically match with each parameter row of the ledger rule table, or if the second detailed parameter fails to logically match with each parameter row of the ledger rule table, then the default parameter row data is extracted for calculation to ensure the successful operation of the assessment element data.

7. A data processing device for bank agency sales business, characterized in that, The device includes: The details acquisition module is used to obtain transaction details and share details of the bank's agency sales business from the data source; The first extraction module is used to extract first basic parameters corresponding to multiple element fields from the transaction details, and at the same time, it is used to extract second basic parameters corresponding to multiple element fields from the share details. The second extraction module is used to extract the first detailed parameters corresponding to multiple element fields from the sales ledger data and to extract the second detailed parameters corresponding to multiple element fields from the overall asset balance data. The first matching module is used to perform logical condition matching between the first basic parameters and each parameter row of the ledger rule table. If the matching is successful, the transaction details are split into data to obtain the sales ledger data. The second basic parameters are also used to perform logical condition matching between the second basic parameters and each parameter row of the ledger rule table. If the matching is successful, the share details are split into data to obtain the overall asset balance data. The ledger rule table includes: a required parameter table, a first non-required parameter table, and a second non-required parameter table. The logical conditions include: a first logical condition, a second logical condition, and a third logical condition. The first logical condition includes: an effective time period and a product difficulty coefficient. The second logical condition includes: the effective time period and the first parameter condition. The third logical condition includes: the effective time period and the second parameter condition. The second matching module is used to perform logical condition matching between the first detailed parameter and the second detailed parameter and each parameter row of the ledger rule table, respectively. The calculation module is used to calculate the target parameter row data that has been successfully matched to obtain the final assessment element data; The step of splitting the transaction details to obtain the sales ledger data includes: When the first basic parameter meets the effective time period and the product difficulty coefficient, the corresponding product sales coefficient and transaction sales coefficient are extracted through the required parameter table. When the first basic parameter meets the effective time period and the first parameter condition, the first non-essential parameter table splits the transaction detail data according to the partial adjustment coefficient of the transaction details, extracts the partial adjustment coefficient of the transaction details, and obtains the first partial adjustment coefficient. The sales ledger data is obtained by multiplying the transaction details data by the product sales coefficient, the transaction sales coefficient, and the first division adjustment coefficient. The step of splitting the share details to obtain the total asset balance data includes: When the second basic parameter meets the effective time period and the second parameter condition, the corresponding overall asset balance coefficient is extracted from the second non-essential parameter table; When the second basic parameter meets the effective time period and the first parameter condition, the first non-essential parameter table splits the share detail data according to the share detail's partial adjustment coefficient, extracts the share detail's partial adjustment coefficient, and obtains the second partial adjustment coefficient. The total asset balance data is obtained by multiplying the share details data by the total asset balance coefficient and the second segment adjustment coefficient.

8. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program that executes the data processing method for bank agency sales business according to any one of claims 1 to 6 when the processor is running.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when run on a processor, executes the data processing method for bank agency sales business as described in any one of claims 1 to 6.

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