Method, device, computer equipment, and storage medium for obtaining data from business reports

Abstract

The present application relates to a method, apparatus, computer equipment, storage medium and computer program product for obtaining data of a business report. The method comprises: obtaining multiple data obtaining formulas corresponding to data obtaining requests; wherein each data obtaining formula comprises multiple formula parameters. Based on the target collection categories corresponding to the respective formula parameters, the multiple formula parameters are combined and classified to obtain multiple classification data obtaining conditions. From the multiple data obtaining formulas, at least one target data obtaining formula corresponding to each classification data obtaining condition is obtained. Based on the formula parameters included in each classification data obtaining condition, the target data packet corresponding to the corresponding classification data obtaining condition is determined. For each classification data obtaining condition, data is obtained from the corresponding target data packet according to the corresponding target data obtaining formula to obtain business data corresponding to each target data obtaining formula; the business data is obtained to form a business report. In this way, the efficiency of obtaining data for business reports is greatly improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, computer equipment, storage medium and computer program product for obtaining data from a business report. Background Art

[0002] With the development of big data technology, it is often necessary to obtain massive amounts of data from the database when processing data. For example, in the process of generating business reports, a large amount of business data needs to be obtained from the database. Among them, the cells of the business report involve a large number of defined data extraction formulas, and a large number of data extraction formulas are needed to complete the data extraction work of the business report.

[0003] In traditional solutions, the parameter conditions of the same data extraction formulas in a large number of data extraction formulas in business reports are often merged, and the maximum condition is used to obtain the business detail data packet. Then, each data extraction formula is filtered and calculated based on this large data packet according to its own parameters to complete the data extraction process.

[0004] However, merging the parameter conditions in the same data extraction formula will lead to a larger data extraction range, making the obtained data packet too large, making it difficult to ensure the accuracy of the data extraction, and the performance of a single data extraction formula is seriously depleted when calculating data based on the data packet. Therefore, there is a problem of low efficiency in extracting data for business reports. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for obtaining data from business reports in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for obtaining data from a business report. The method comprises:

[0007] Acquire multiple data acquisition formulas corresponding to the data acquisition request; wherein each data acquisition formula includes multiple formula parameters;

[0008] Obtaining target collection categories corresponding to each formula parameter, and combining multiple formula parameters based on the target collection categories corresponding to each formula parameter to obtain multiple classification data collection conditions;

[0009] From the multiple data acquisition formulas, obtain at least one target data acquisition formula corresponding to each classification data acquisition condition;

[0010] Based on the formula parameters included in each classification data acquisition condition, determine the target data packet corresponding to the corresponding classification data acquisition condition;

[0011] For each classification data acquisition condition, data is acquired from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula to obtain business data corresponding to each target data acquisition formula; the acquired business data is used to form a business report.

[0012] In a second aspect, the present application also provides a data acquisition device for a business report. The device comprises:

[0013] A first acquisition module is used to acquire multiple data acquisition formulas corresponding to the data acquisition request; wherein each data acquisition formula includes multiple formula parameters;

[0014] An acquisition module is used to obtain target collection categories corresponding to each formula parameter, and based on the target collection categories corresponding to each formula parameter, to collect and combine multiple formula parameters to obtain multiple classification data collection conditions;

[0015] A second acquisition module is used to acquire at least one target data acquisition formula corresponding to each classification data acquisition condition from the multiple data acquisition formulas;

[0016] A determination module, used to determine a target data packet corresponding to a corresponding classification data acquisition condition based on a formula parameter included in each classification data acquisition condition;

[0017] The data acquisition module is used to acquire data from the target data packet corresponding to each classification data acquisition condition according to the corresponding target data acquisition formula for each classification data acquisition condition, and obtain the business data corresponding to each target data acquisition formula; the acquired business data is used to form a business report.

[0018] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0019] Acquire multiple data acquisition formulas corresponding to the data acquisition request; wherein each data acquisition formula includes multiple formula parameters;

[0020] Obtaining target collection categories corresponding to each formula parameter, and combining multiple formula parameters based on the target collection categories corresponding to each formula parameter to obtain multiple classification data collection conditions;

[0021] From the multiple data acquisition formulas, obtain at least one target data acquisition formula corresponding to each classification data acquisition condition;

[0022] Based on the formula parameters included in each classification data acquisition condition, determine the target data packet corresponding to the corresponding classification data acquisition condition;

[0023] For each classification data acquisition condition, data is acquired from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula to obtain business data corresponding to each target data acquisition formula; the acquired business data is used to form a business report.

[0024] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0025] Acquire multiple data acquisition formulas corresponding to the data acquisition request; wherein each data acquisition formula includes multiple formula parameters;

[0026] Obtaining target collection categories corresponding to each formula parameter, and combining multiple formula parameters based on the target collection categories corresponding to each formula parameter to obtain multiple classification data collection conditions;

[0027] From the multiple data acquisition formulas, obtain at least one target data acquisition formula corresponding to each classification data acquisition condition;

[0028] Based on the formula parameters included in each classification data acquisition condition, determine the target data packet corresponding to the corresponding classification data acquisition condition;

[0029] For each classification data acquisition condition, data is acquired from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula to obtain business data corresponding to each target data acquisition formula; the acquired business data is used to form a business report.

[0030] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0031] Acquire multiple data acquisition formulas corresponding to the data acquisition request; wherein each data acquisition formula includes multiple formula parameters;

[0032] Obtaining target collection categories corresponding to each formula parameter, and combining multiple formula parameters based on the target collection categories corresponding to each formula parameter to obtain multiple classification data collection conditions;

[0033] From the multiple data acquisition formulas, obtain at least one target data acquisition formula corresponding to each classification data acquisition condition;

[0034] Based on the formula parameters included in each classification data acquisition condition, determine the target data packet corresponding to the corresponding classification data acquisition condition;

[0035] For each classification data acquisition condition, data is acquired from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula to obtain business data corresponding to each target data acquisition formula; the acquired business data is used to form a business report.

[0036] The data acquisition method, device, computer equipment, storage medium and computer program product of the above-mentioned business report are obtained by acquiring multiple data acquisition formulas corresponding to the data acquisition request; wherein each data acquisition formula includes multiple formula parameters. The target collection categories corresponding to each formula parameter are obtained, and based on the target collection categories corresponding to each formula parameter, the multiple formula parameters are collected and combined to obtain multiple classified data acquisition conditions. In this way, in combination with the target collection categories corresponding to each formula parameter, the formula parameters can be collected and combined more targeted to obtain more accurate classified data acquisition conditions. From the multiple data acquisition formulas, at least one target data acquisition formula corresponding to each classified data acquisition condition is obtained. In this way, each data acquisition formula can be accurately divided into the adapted data acquisition conditions, so that each data acquisition condition contains the corresponding target data acquisition formula. Based on the formula parameters included in each classified data acquisition condition, the target data packet corresponding to the corresponding classified data acquisition condition is determined. In this way, for each classified data acquisition condition, the target data packet corresponding to the corresponding data acquisition condition can be adapted to the target data acquisition formula, so that each target data packet can accurately determine the corresponding target data acquisition formula. For each classification data acquisition condition, data is acquired from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula, and the business data corresponding to each target data acquisition formula is obtained; the acquired business data is used to form a business report. In this way, based on the target data acquisition formula corresponding to each target data packet, the corresponding business data can be acquired quickly and accurately. Compared with the traditional large data packet acquisition, the present invention can obtain more accurate classification data acquisition conditions based on the target collection category corresponding to each formula parameter. In this way, a target data packet with a more accurate and detailed acquisition range can be obtained based on the classification data acquisition conditions. Therefore, each data acquisition formula is parsed and acquired from the corresponding target data packet with a small data volume, which can greatly improve the data acquisition accuracy while ensuring the reduction of the data acquisition time, thereby greatly improving the data acquisition efficiency of the business report. In addition, compared with the traditional data packet acquisition process, the present application determines the target data packet according to the formula parameters actually contained in each classification data acquisition condition, and can realize on-demand data acquisition, reducing the consumption of resources such as memory, database, and network transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is an application environment diagram of a method for obtaining data of a business report in one embodiment;

[0038] Figure 2A flowchart of a method for obtaining data from a business report in one embodiment;

[0039] Figure 3 A schematic diagram of a flow chart of a step of determining a classification and data acquisition condition in one embodiment;

[0040] Figure 4 It is a flowchart of a method for obtaining data from a business report in another embodiment;

[0041] Figure 5 It is a flowchart of a method for obtaining data from a business report in another embodiment;

[0042] Figure 6 It is a structural block diagram of a data acquisition device for a business report in one embodiment;

[0043] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] The data acquisition method of the business report provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 and the server 104 can be used alone to execute the data acquisition method of the business report, or they can be used together to execute the data acquisition method of the business report. Taking the terminal 102 and the server 104 as an example of the data acquisition method for executing the business report in collaboration, the terminal 102 sends a data acquisition request to the server 104, and the server 104 obtains multiple data acquisition formulas corresponding to the data acquisition request; wherein each data acquisition formula includes multiple formula parameters. The server 104 obtains the target collection categories corresponding to each formula parameter, and based on the target collection categories corresponding to each formula parameter, the multiple formula parameters are collected and combined to obtain multiple classified data acquisition conditions. From the multiple data acquisition formulas, the server 104 obtains at least one target data acquisition formula corresponding to each classified data acquisition condition. Based on the formula parameters included in each classification data acquisition condition, the server 104 determines the target data packet corresponding to the corresponding classification data acquisition condition. For each classification data acquisition condition, the server 104 extracts data from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula, and obtains the business data corresponding to each target data acquisition formula; the extracted business data is used to form a business report. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, etc. The server 104 can be implemented as an independent server or a server cluster consisting of multiple servers.

[0046] In one embodiment, Figure 2 As shown, a method for obtaining data from a business report is provided, and the method is applied to a computer device as an example for explanation. The computer device may be Figure 1 The method for obtaining data from the business report includes the following steps:

[0047] Step S202, obtaining multiple data retrieval formulas corresponding to the data retrieval request; wherein each data retrieval formula includes multiple formula parameters.

[0048] The data extraction formula refers to the formula configured in the financial report. The type of the data extraction formula can be Acct formula, BGData formula, etc. The Acct formula refers to the data extraction formula for extracting the beginning, occurrence, and ending balances of the general ledger account. The BGData formula extracts the budgeted numbers compiled in the budget report. The formula parameters are the parameters that constitute the data extraction formula. The formula parameters can be year, period, account, accounting dimension, currency, etc. For example, the format of the Acct data extraction formula is: Acct("","","Y","","","",""), and the formula parameters included are account book, account, data extraction type, currency, year, start period, and end period. Y is the ending balance.

[0049] Specifically, the computer device determines a target category of the data acquisition formula based on the data acquisition request initiated by the target object, and obtains multiple data acquisition formulas of the target category corresponding to the data acquisition request based on the target category.

[0050] For example, the computer device determines that the target category of the data acquisition formula is the Acct data acquisition formula based on the data acquisition request initiated by the target object. The computer device obtains 10 data acquisition formulas based on the Acct data acquisition formula. As shown in Table 1:

[0051] Table 1 List of data acquisition formulas

[0052]

[0053] The accounting dimension is the scope of the project that needs to be accounted for, and defines the dimension that the account needs to be auxiliary accounted for. You can select any basic data, auxiliary data, etc. in the system as the accounting dimension. For example, basic data such as customers and suppliers can be used as accounting dimensions, or auxiliary data such as delivery methods can be used as auxiliary accounting for accounts. The number corresponding to each formula parameter represents the sub-parameter in the corresponding formula parameter. For example, the account book formula parameter contains two sub-parameters, namely 001 and 002.

[0054] Step S204, obtaining target collection categories corresponding to the respective formula parameters, and combining the multiple formula parameters based on the target collection categories corresponding to the respective formula parameters to obtain multiple classification data collection conditions.

[0055] The aggregation category is the aggregation method, and the aggregation category is the aggregation according to certain standards. The classification data retrieval condition is the screening condition for filtering business data from the database.

[0056] Specifically, the computer device obtains the target collection categories corresponding to the respective formula parameters, and based on the target collection categories corresponding to the respective formula parameters, the formula parameters are collected and combined to obtain respective formula parameter combinations. Based on each formula parameter combination, the computer device generates a classification data collection condition corresponding to the corresponding formula parameter combination.

[0057] For example, the computer device obtains the target collection categories corresponding to the respective formula parameters, and based on the collection order of each target collection category, collects and combines the multiple formula parameters corresponding to each target collection category to obtain each formula parameter combination. For each formula parameter combination, the computer device generates a classification data acquisition condition corresponding to the corresponding formula parameter combination.

[0058] Step S206, obtaining at least one target data extraction formula corresponding to each classification data extraction condition from the multiple data extraction formulas.

[0059] Specifically, the computer device obtains each classification number-taking condition, and based on each classification number-taking condition, the computer device obtains at least one target number-taking formula corresponding to each classification number-taking condition from a plurality of number-taking formulas.

[0060] Step S208, based on the formula parameters included in each classification data acquisition condition, determine the target data packet corresponding to the corresponding classification data acquisition condition.

[0061] The target data packet is characterized as a data acquisition packet, wherein each target data includes a plurality of business data.

[0062] Specifically, the computer device obtains each classification data acquisition condition, and for each classification data acquisition condition, the computer device obtains business data from the database based on the formula parameters included in the corresponding classification data acquisition condition to obtain a target data packet corresponding to the corresponding classification data acquisition condition.

[0063] For example, for the classification data retrieval condition A, the formula parameters in the classification data retrieval condition A are account book 001 and subjects. The computer device obtains the business data belonging to account book 001 and all subjects from the database based on the formula parameters included in the classification data retrieval condition A, and determines the target data packet corresponding to the classification data retrieval condition A based on multiple business data.

[0064] Step S210, for each classification data acquisition condition, data is acquired from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula to obtain business data corresponding to each target data acquisition formula; the acquired business data is used to form a business report.

[0065] The business report may be a financial report, such as a balance sheet, an income statement, etc. The business data is data related to the business report, such as a character code or a number corresponding to each formula parameter.

[0066] Specifically, for each classification data acquisition condition, the computer device determines the target data packet corresponding to the corresponding classification data acquisition condition and the target data acquisition formula. For each classification data acquisition formula, the computer device acquires data from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula to obtain business data corresponding to each target data acquisition formula. The acquired business data is used to form a business report.

[0067] For example, for the classification data acquisition condition A, the target data acquisition formula corresponding to the classification data acquisition condition A is formula 1 to formula 7, and the corresponding target data packet is target data packet 1. For the classification data acquisition condition B, the target data acquisition formula corresponding to the classification data acquisition condition B is formula 3 to formula 4, and the corresponding target data packet is target data packet 2. For each data acquisition formula in formula 1 to formula 7, data is acquired in target data packet 1 to obtain the business data corresponding to the corresponding data acquisition formula. For each data acquisition formula in formula 3 to formula 4, data is acquired in target data packet 2 to obtain the business data corresponding to the corresponding data acquisition formula.

[0068] In the data acquisition method of the above-mentioned business report, multiple data acquisition formulas corresponding to the data acquisition request are acquired; wherein each data acquisition formula includes multiple formula parameters. The target collection categories corresponding to each formula parameter are acquired, and based on the target collection categories corresponding to each formula parameter, the multiple formula parameters are collected and combined to obtain multiple classified data acquisition conditions. In this way, in combination with the target collection categories corresponding to each formula parameter, the formula parameters can be collected and combined more specifically to obtain more accurate classified data acquisition conditions. From the multiple data acquisition formulas, at least one target data acquisition formula corresponding to each classified data acquisition condition is acquired. In this way, each data acquisition formula can be accurately divided into the adapted data acquisition conditions, so that each data acquisition condition contains the corresponding target data acquisition formula. Based on the formula parameters included in each classified data acquisition condition, the target data packet corresponding to the corresponding classified data acquisition condition is determined. In this way, for each classified data acquisition condition, the target data packet corresponding to the corresponding data acquisition condition can be adapted to the target data acquisition formula, so that each target data packet can accurately determine the corresponding target data acquisition formula. For each classification data acquisition condition, data is acquired from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula, and the business data corresponding to each target data acquisition formula is obtained; the acquired business data is used to form a business report. In this way, based on the target data acquisition formula corresponding to each target data packet, the corresponding business data can be acquired quickly and accurately. Compared with the traditional large data packet acquisition, the present invention can obtain more accurate classification data acquisition conditions based on the target collection category corresponding to each formula parameter. In this way, a target data packet with a more accurate and detailed acquisition range can be obtained based on the classification data acquisition conditions. Therefore, each data acquisition formula is parsed and acquired from the corresponding target data packet with a small data volume, which can greatly improve the data acquisition accuracy while ensuring the reduction of the data acquisition time, thereby greatly improving the data acquisition efficiency of the business report. In addition, compared with the traditional data packet acquisition process, the present application determines the target data packet according to the formula parameters actually contained in each classification data acquisition condition, and can realize on-demand data acquisition, reducing the consumption of resources such as memory, database, and network transmission. In one embodiment, before obtaining the target collection categories corresponding to each formula parameter, it also includes: combining multiple formula parameters and multiple collection categories to obtain multiple collection schemes; wherein each formula parameter in each collection scheme corresponds to a collection category. For each collection scheme, data is obtained through the corresponding collection scheme, and the data collection time of the corresponding collection scheme is counted. Based on the data collection time corresponding to each collection scheme, a target collection scheme is determined from multiple collection schemes; wherein the target collection scheme contains the target collection categories corresponding to each formula parameter.

[0069] Specifically, the computer device obtains multiple formula parameters and multiple collection categories. For each formula parameter, the computer device arbitrarily selects a collection category from the multiple collection categories as the collection category of the corresponding formula parameter, and determines multiple different collection schemes. For each collection scheme, the computer device obtains data through the collection scheme, and counts the data collection time of the corresponding collection scheme. The computer device sorts the data collection time of each collection scheme to obtain a time series. Based on the time series, the computer device determines a target collection scheme from the multiple collection schemes.

[0070] For example, the computer device obtains 6 formula parameters and 3 collection categories, wherein the formula parameters are account book, account, accounting dimension, year, currency, data collection category, and the collection categories are A, B, and C. For each formula parameter in each collection scheme, a collection category is selected from the three collection categories as the collection category of the corresponding formula parameter in the corresponding collection scheme, and m different collection schemes are determined. For example, for collection scheme b, for the account book, the computer device selects collection category A from the three collection categories as the collection category of the account book, and determines the collection categories of other formula parameters in turn to form collection scheme b. For each collection scheme, data is collected through the corresponding collection scheme, and the data collection time of the corresponding collection scheme is counted. The computer device sorts the data collection time from small to large to obtain a time series. The computer device takes the collection scheme corresponding to the first time sort (the smallest data collection time) as the target collection scheme.

[0071] In this embodiment, multiple formula parameters and multiple collection categories are combined to obtain multiple collection schemes; wherein each formula parameter in each collection scheme corresponds to a collection category. For each collection scheme, data is obtained through the corresponding collection scheme, and the data collection time of the corresponding collection scheme is counted. Based on the data collection time corresponding to each collection scheme, a target collection scheme is determined from multiple collection schemes; wherein the target collection scheme contains target collection categories corresponding to each formula parameter. In this way, by determining the target collection scheme through the time dimension, it can be ensured that when data is obtained based on the target collection scheme, the speed of obtaining data for business reports is improved. Therefore, when faced with a huge number of data collection formulas, time costs can be greatly saved. In addition, multiple collection schemes can be flexibly selected for the same data collection formula, which greatly increases the intelligence in the process of obtaining data for business reports.

[0072] In one embodiment, multiple formula parameters and multiple collection categories are combined to obtain multiple collection schemes; wherein each formula parameter in each collection scheme corresponds to a collection category. For each collection scheme, data is collected through the corresponding collection scheme, the data collection result of the corresponding collection scheme is determined, and the data collection time of the corresponding collection scheme is counted. Based on the data collection results and data collection times corresponding to each collection scheme, a target collection scheme is determined from multiple collection schemes; wherein the target collection scheme contains target collection categories corresponding to each formula parameter.

[0073] Specifically, the computer device obtains multiple formula parameters and multiple collection categories. For each formula parameter, the computer device arbitrarily selects a collection category from the multiple collection categories as the collection category of the corresponding formula parameter, and determines multiple different collection schemes. For each collection scheme, the computer device extracts data through the collection scheme, obtains the data extraction result of the corresponding collection scheme, and counts the data extraction time of the corresponding collection scheme. The computer device determines the accuracy of each collection scheme based on the data extraction results corresponding to each collection scheme. The computer device sorts the accuracy of each collection scheme to obtain an accuracy sequence, and sorts the data extraction time of each collection scheme to obtain a time series. The computer device determines the target collection scheme from multiple collection schemes based on the accuracy sequence and the time series.

[0074] For example, the computer device obtains 6 formula parameters and 3 collection categories, wherein the formula parameters are account book, subject, accounting dimension, year, currency, data collection category, and the collection categories are A, B, and C. For each formula parameter in each collection scheme, a collection category is selected from the three collection categories as the collection category of the corresponding formula parameter in the corresponding collection scheme, and m different collection schemes are determined. For example, for collection scheme b, for the account book, the computer device selects collection category A from the three collection categories as the collection category of the account book, and determines the collection categories of other formula parameters in turn to form collection scheme b. The computer device obtains the data collection results corresponding to each collection scheme, obtains m data collection results, and determines the accuracy of each collection scheme based on the m data collection results. The computer device obtains the data collection time of each collection scheme, sorts the data collection time from small to large, obtains a time series, and sorts the accuracy from large to small, obtains an accuracy series. For each imputation scheme, determine the first serial number of the corresponding imputation scheme in the time series and the second serial number in the accuracy, determine the evaluation value of each imputation scheme, and determine the target imputation scheme based on each evaluation value. The evaluation value of each imputation scheme can be determined based on the first serial number and the second serial number, by average value calculation or difference calculation, without limitation.

[0075] In this embodiment, multiple formula parameters and multiple collection categories are combined to obtain multiple collection schemes; wherein each formula parameter in each collection scheme corresponds to a collection category. For each collection scheme, data is collected through the corresponding collection scheme, the data collection results of the corresponding collection scheme are determined, and the data collection time of the corresponding collection scheme is counted. Based on the data collection results and data collection times corresponding to each collection scheme, a target collection scheme is determined from multiple collection schemes. In this way, the target collection scheme selected by combining the two dimensions of time dimension and accuracy dimension can ensure high accuracy and rapid data collection speed at the same time, thereby improving the efficiency of data collection for business reports. In addition, multiple collection schemes can be flexibly selected for the same data collection formula, which greatly increases the intelligence in the process of data collection for business reports.

[0076] In one embodiment, Figure 3 As shown, the collection categories include non-collection categories, conditional collection categories and custom collection categories. Based on the target collection categories corresponding to the respective formula parameters, multiple formula parameters are combined and classified to obtain multiple data collection conditions, including:

[0077] Step S302: determining a first sub-parameter in a first formula parameter that belongs to a non-aggregation category based on a plurality of data acquisition formulas and the target aggregation category corresponding to each formula parameter.

[0078] Among them, the non-aggregation category means that the formula parameters corresponding to the non-aggregation category are not aggregated.

[0079] Specifically, the computer device obtains multiple data acquisition formulas and target collection categories corresponding to each formula parameter. The computer device takes the formula parameter belonging to the non-collection category as the first formula parameter, and determines the first sub-parameter corresponding to the first formula parameter based on the multiple first formula parameters.

[0080] For example, the target collection categories corresponding to the formula parameters are shown in Table 2, where the target collection category of the account book is the non-collection category, and the account book formula parameter is the first formula parameter. Among the multiple data extraction formulas, the account book formula parameter is divided into 001 account book and 002 account book, and the computer device determines that the first sub-parameters of the first formula parameter belonging to the non-collection category are 001 account book and 002 account book.

[0081] Table 2 Target aggregation scheme

[0082]

[0083]

[0084] Step S304: determining a non-reduction set corresponding to the corresponding first sub-parameter based on a plurality of data extraction formulas including the same first sub-parameter.

[0085] Specifically, the computer device obtains multiple number-taking formulas and each first sub-parameter, and based on each first sub-parameter, divides the multiple number-taking formulas including the same first sub-parameter into non-aggregate sets corresponding to the corresponding first sub-parameters.

[0086] For example, the computer device obtains 10 data acquisition formulas, wherein the target collection category of the account book formula parameter belongs to the non-collection category and is the first sub-parameter. There are two first sub-parameters in the 10 data acquisition formulas, namely account book 001 and account book 002, wherein account book 001 corresponds to formulas 1 to 7, and account book 002 corresponds to formulas 8 to 10. The computer device uses formulas 1 to 7 including the first sub-parameter of account book 001 as the non-collection set corresponding to the first sub-parameter of account book 001, and uses formulas 8 to 10 including the first sub-parameter of account book 002 as the non-collection set corresponding to the first sub-parameter of account book 002. Among them, a huge number of data acquisition formulas and a huge amount of business data are involved in the data acquisition process of business reports.

[0087] Step S306: for each non-aggregation set, determine whether there is a second formula parameter belonging to the user-defined aggregation category in the data extraction formula in the corresponding non-aggregation set.

[0088] The custom collection category is a customizable collection method, wherein multiple formula parameters belonging to the custom collection category can be freely combined, for example, two-by-two combinations, three-by-three combinations, etc.

[0089] Specifically, the computer device obtains a plurality of non-aggregation sets, and for each non-aggregation set, the computer device determines whether a second formula parameter belonging to a custom aggregation category exists in a data acquisition formula in the corresponding non-aggregation set.

[0090] For example, as shown in Table 2, the target collection categories of the account formula parameter and the accounting dimension parameter both belong to the custom collection category, and the account and the accounting dimension are both second formula parameters. The computer device obtains two non-collection sets, namely, the non-collection set corresponding to the first sub-parameter of account book 001 and the non-collection set corresponding to the first sub-parameter of account book 002. The computer device determines whether there is a second formula parameter of the account or a second formula parameter of the accounting dimension in each non-collection set.

[0091] Step S308: If the second formula parameter exists in the number-taking formula in the non-aggregated set, at least one first number-taking combination is determined based on the first sub-parameter and the second formula parameter.

[0092] Specifically, if the second formula parameter exists in the number extraction formula in the non-aggregated set, the computer device obtains the first sub-parameter and the second formula parameter, combines the first sub-parameter and the second formula parameter, and obtains at least one first number extraction combination.

[0093] Step S310: for each first data acquisition combination, based on the data acquisition formula in the corresponding first data acquisition combination, it is determined whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional aggregation category.

[0094] Among them, conditional aggregation is aggregation according to the aggregation conditions corresponding to the corresponding formula parameters. For example, when the period formula parameter adopts conditional aggregation, aggregation is performed according to the predetermined time period; when the currency formula parameter adopts conditional aggregation, aggregation is performed according to the currency type.

[0095] Specifically, the computer device obtains multiple first number acquisition combinations, and for each first number acquisition combination, the computer device determines whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional collection category based on the number acquisition formula in the corresponding number acquisition combination.

[0096] For example, as shown in Table 2, the target collection categories of the term formula parameter and the currency formula parameter are both conditional collection, and the term formula parameter and the currency formula parameter are both third formula parameters. For the first data acquisition combination P1 composed of the first sub-parameter being the account book 001 and the second formula parameter being the account, if the first data acquisition combination P1 has data acquisition formulas 1 to 7, the computer device determines whether there are at least two different terms in the term third formula parameter, that is, whether there are at least two different term third sub-parameters, and determines whether there are at least two different currencies in the currency third formula parameter, that is, whether there are at least two different currency third sub-parameters, based on the 7 data acquisition formulas in the first data acquisition combination P1.

[0097] Step S312: If it does not exist, the corresponding classification data acquisition condition is determined directly based on the formula parameters included in the first data acquisition combination.

[0098] Specifically, if at least two different third sub-parameters do not exist in any third formula parameter belonging to the conditional collection category, the computer device obtains the first data acquisition combination, and directly determines the corresponding classification data acquisition condition based on each formula parameter included in the first data acquisition combination. For example, if at least two different third sub-parameters do not exist in any third formula parameter belonging to the conditional collection category, the first data acquisition combination is obtained. For example, for one of the first data acquisition combinations P1, the first data acquisition combination includes the first sub-parameter of account book 001 and the second formula parameter of account, then the computer device determines the classification data acquisition condition Q1 corresponding to the first data acquisition combination based on the first data acquisition combination P1. Among them, the classification condition Q1 is to extract data from the business data belonging to account book 001 and all accounts.

[0099] In this embodiment, based on multiple number-taking formulas and the target collection categories corresponding to each formula parameter, the first sub-parameter in the first formula parameter belonging to the non-collection category is determined. Based on multiple number-taking formulas including the same first sub-parameter, the non-collection set corresponding to the corresponding first sub-parameter is determined, so that different first sub-parameters can be directly classified, and based on each first sub-parameter, multiple number-taking formulas can be quickly divided according to each first sub-parameter. For each non-collection set, it is determined whether there is a second formula parameter belonging to the custom collection category in the number-taking formula in the corresponding non-collection set. If the second formula parameter exists in the number-taking formula in the non-collection set, at least one first number-taking combination is determined based on the first sub-parameter and the second formula parameter. In this way, based on the second formula parameter and the corresponding first sub-parameter determined for the custom collection, multiple different first number-taking combinations can be combined more specifically, so that the first number-taking combination is more comprehensive. For each first number-taking combination, based on the number-taking formula in the corresponding first number-taking combination, it is determined whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional collection category. If it does not exist, the corresponding classification data collection condition is directly determined based on each formula parameter included in the first data collection combination. In this way, in combination with the target collection category corresponding to each formula parameter, the formula parameters can be more specifically collected and combined to obtain more accurate classification data collection conditions.

[0100] In one embodiment, the determining of at least one first number-taking combination based on the first sub-parameter and the second formula parameter includes: combining each second formula parameter with the corresponding first sub-parameter to obtain a first parameter combination. Combining the second formula parameters to obtain a plurality of custom combinations, and combining each custom combination with the corresponding first sub-parameter to obtain a second parameter combination. The first parameter combination and the second parameter combination are used as the first number-taking combination.

[0101] Specifically, the computer device obtains the first sub-parameter and the second formula parameter, and for each second formula parameter, combines the corresponding second formula parameter with each first sub-parameter to obtain a first parameter combination. The computer device combines each second formula parameter to obtain a second parameter combination. The computer device uses the first parameter combination and the second parameter combination as a first data acquisition combination.

[0102] For example, when the first sub-parameter includes account book 001 and account book 002, and the second formula parameter is a subject and a currency, the non-aggregated set corresponding to account book 001 and account book 002 contains the second formula parameter. The computer device combines each second formula parameter with the corresponding first sub-parameter to obtain a first parameter combination, which can be account book 001+subject, account book 001+currency, account book 002+subject, account book 002+currency. The computer device combines each second parameter with each other to obtain a second parameter combination, which can be subject+currency. Among them, if there are at least three second formula parameters, the mutual combination can be a combination of two or three. The computer device uses account book 001+subject, account book 001+currency, account book 002+subject, account book 002+currency, subject+currency as the first data extraction combination.

[0103] It should be noted that if the second formula parameter contains accounts and accounting dimensions, since the accounting dimension is related to the account, it cannot be combined with the first sub-parameter alone. Therefore, the first parameter combination is account book 001 + account, account book 002 + account; the second parameter combination is account book 001 + account + accounting dimension, account book 002 + account + accounting dimension.

[0104] In this embodiment, each second formula parameter is combined with the corresponding first sub-parameter to obtain a first parameter combination. The second formula parameters are combined to obtain multiple custom combinations, and each custom combination is combined with the corresponding first sub-parameter to obtain a second parameter combination. The first parameter combination and the second parameter combination are used as the first number-taking combination. In this way, based on determining the second formula parameter of the custom collection and the corresponding first sub-parameter, multiple different first number-taking combinations can be combined in a more targeted manner, making the first number-taking combination more comprehensive.

[0105] In one embodiment, if the second formula parameter does not exist in the number-taking formula in the non-collection set, then based on the number-taking formula in the corresponding non-collection set, it is determined whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional collection category. If so, the third formula parameter with at least two different third sub-parameters is used as the first condition parameter, and each third sub-parameter in each first condition parameter is combined to obtain multiple first condition combinations, and each first condition combination is combined with the first sub-parameter to obtain a second number-taking combination. Based on each formula parameter included in the second number-taking combination, the corresponding classification number-taking condition is determined.

[0106] Specifically, if the second formula parameter does not exist in the number-taking formula in the non-collection set, the computer device determines whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional collection category based on the number-taking formula in the corresponding non-collection set. If so, the computer device will use the third formula parameter with at least two different third sub-parameters as the first condition parameter, and determine each third sub-parameter in the first condition parameter. The computer device combines each third sub-parameter in each first condition parameter to obtain multiple first condition combinations. The computer device obtains the first sub-parameter in the corresponding non-collection set, and combines each first condition combination with the first sub-parameter to obtain multiple second number-taking combinations. The computer device determines the corresponding classification number-taking condition based on each formula parameter included in the second number-taking combination.

[0107] For example, for the non-aggregated set corresponding to the first sub-parameter of account book 002, the non-aggregated set includes number-taking formulas 8 to 10. Among them, the conditional aggregation categories are year and currency, that is, the third formula parameters are year and currency. Based on the number-taking formula, the computer device determines whether there are two different third sub-parameters in the third formula parameter of the year. If so, the third formula parameter of the year is used as the first condition parameter. Among them, the third formula parameter of the currency has only one currency, such as currency X. Based on number-taking formulas 8 to 10, the computer device determines that each third sub-parameter in the first condition parameter is the year 2021.1 and 2020.2. Then the first condition combination is 2021.1+currency X, 2021.2+currency X. The computer device combines each first condition combination with the first sub-parameter to obtain multiple second number-taking combinations, which can be account book 002+2021.1+currency X, account book 002+2021.2+currency X. The computer device determines the corresponding classification data acquisition conditions based on the formula parameters included in each second data acquisition combination.

[0108] In this embodiment, if the second formula parameter does not exist in the number-taking formula in the non-collection set, then based on the number-taking formula in the corresponding non-collection set, it is determined whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional collection category. If so, the third formula parameter with at least two different third sub-parameters is used as the first condition parameter, and each third sub-parameter in each first condition parameter is combined to obtain a plurality of first condition combinations, and each first condition combination is combined with the first sub-parameter to obtain a second number-taking combination. Based on the formula parameters included in the second number-taking combination, the corresponding classification number-taking condition is determined. In this way, in combination with the target collection category corresponding to each formula parameter, the formula parameters can be more specifically collected and combined to obtain more accurate classification number-taking conditions.

[0109] In one embodiment, if Figure 4 As shown, based on the target collection categories corresponding to the 10 data-collecting formulas and the formula parameters, as shown in Table 2, the first sub-parameters are determined to be account book 001 and account book 002. And based on the two first sub-parameters, the 10 data-collecting formulas are divided into two non-collection sets, namely, the first non-collection set corresponding to account book 001, including formulas 1 to 7; and the second non-collection set corresponding to account book 002, including formulas 8 to 10. For each non-collection set, it is determined whether the data-collecting formula in the corresponding non-collection set has a second formula parameter belonging to the custom collection category, namely, the account and the accounting dimension, and it is determined whether the first non-collection set contains the second formula parameter.

[0110] For the data extraction formulas in the first non-reduction set, determine the first data extraction combination, that is, account book 001+account, account book 001+account+accounting dimension. For the multiple data extraction formulas corresponding to the first non-reduction set, if there are no at least two different third sub-parameters in any third formula parameter belonging to the conditional collection category, then based on the first data extraction combination account book 001+account, determine the classification data extraction condition Y1, and determine data package 1 based on the classification data extraction condition Y1; based on the first data extraction combination account book 001+account+accounting dimension, determine the classification data extraction jump Y2, and determine data package 2 based on the classification data extraction condition Y2. Formula 1, Formula 2, Formula 5, Formula 6, and Formula 7 correspond to account book 001+account, and then extract data from data package 1 to obtain the corresponding results. Formula 3 and Formula 4 correspond to account book 001+account+accounting dimension, and then extract data from data package 2 to obtain the corresponding results. For the second non-collection set, if there is no second formula parameter belonging to the custom collection category, then it is determined whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional collection category, and it is obtained that there are two different third sub-parameters for the third formula parameter of the year, which are period 2021.1 and 2021.2. The third formula parameter of the year is the first conditional parameter, and the second data extraction combination is determined, that is, account book 002 + period 2021.1, account book 002 + period 2021.2. Data packet 3 is determined based on account book 002 + period 2021.1, corresponding to formula 8 and formula 9 to extract data from data packet 3. Data packet 4 is determined based on account book 002 + period 2021.2, corresponding to formula 10 to extract data from data packet 4.

[0111] In this embodiment, multiple formula parameters are grouped and combined based on the target collection categories corresponding to each formula parameter to obtain multiple classified data acquisition conditions. In this way, in combination with the target collection categories corresponding to each formula parameter, the formula parameters can be grouped and combined more specifically to obtain more accurate classified data acquisition conditions. From the multiple data acquisition formulas, at least one target data acquisition formula corresponding to each classified data acquisition condition is obtained. In this way, each data acquisition formula can be accurately divided into the adapted data acquisition conditions, so that each data acquisition condition contains the corresponding target data acquisition formula. Based on the formula parameters included in each classified data acquisition condition, the target data packet corresponding to the corresponding classified data acquisition condition is determined. In this way, for each classified data acquisition condition, the target data packet corresponding to the corresponding data acquisition condition can be adapted to the target data acquisition formula, so that each target data packet can accurately determine the corresponding target data acquisition formula. For each classified data acquisition condition, data is acquired from the target data packet corresponding to the corresponding classified data acquisition condition according to the corresponding target data acquisition formula to obtain business data corresponding to each target data acquisition formula; the acquired business data is used to form a business report. In this way, based on the target data acquisition formula corresponding to each target data packet, the corresponding business data can be obtained quickly and accurately, which not only ensures the reduction of data acquisition time, but also greatly improves the data acquisition accuracy, thereby greatly improving the data acquisition efficiency of business reports.

[0112] In one embodiment, if there are at least two third formula parameters with different third sub-parameters in the third formula parameters belonging to the conditional collection category based on the number-taking formula in the corresponding first number-taking combination, then the third formula parameters with at least two different third sub-parameters are used as the second condition parameters. The third sub-parameters in the second condition parameters are combined to obtain multiple second condition combinations, and each second condition combination is combined with the formula parameters in the first number-taking combination to obtain a third number-taking combination. Based on the formula parameters included in the third number-taking combination, the corresponding classification number-taking condition is determined.

[0113] Specifically, if there are at least two different third sub-parameters of the third formula parameter in the third formula parameter belonging to the conditional collection based on the number extraction formula in the corresponding first number extraction combination, the computer device will use the third formula parameter with at least two different third sub-parameters as the second condition parameter, and determine each third sub-parameter in the second condition parameter. The computer device combines each third sub-parameter in each second condition parameter to obtain multiple second condition combinations. The computer device obtains the formula parameters obtained in the first number extraction combination, and combines each second condition combination with the formula parameters in the first number extraction combination to obtain a third number extraction combination. The computer device determines the corresponding classification number extraction condition based on each formula parameter included in the third number extraction combination.

[0114] In this embodiment, if there are at least two third formula parameters with different third sub-parameters in the third formula parameters belonging to the conditional collection category based on the number-taking formula in the corresponding first number-taking combination, then the third formula parameters with at least two different third sub-parameters will be used as the second conditional parameters. The third sub-parameters in the second conditional parameters are combined to obtain multiple second conditional combinations, and each second conditional combination is combined with the formula parameters in the first number-taking combination to obtain a third number-taking combination. Based on the formula parameters included in the third number-taking combination, the corresponding classification number-taking conditions are determined. In this way, combined with the target collection category corresponding to each formula parameter, the formula parameters can be more specifically collected and combined to obtain more accurate classification number-taking conditions.

[0115] In order to facilitate a clearer understanding of the technical solution of the present application, a more detailed embodiment is provided for description. Figure 5As shown, the customer sets a formula point recalculation request in the report. Based on the recalculation request, the computer device runs the intelligent parameter collection model. Specifically, the type of each data extraction formula is determined through the formula engine formula classification collection, such as Acct, AcctCash, and BGData. The type of the target data extraction formula is determined to be Acct from the point recalculation request, and multiple data extraction formulas corresponding to the type are obtained. The computer device obtains multiple formula parameters and multiple collection categories. For each formula parameter, the computer device arbitrarily selects a collection category from the multiple collection categories as the collection category of the corresponding formula parameter, and determines multiple different collection schemes. For each collection scheme, the computer device extracts data through the collection scheme and counts the data extraction time of the corresponding collection scheme. The computer device sorts the data extraction time of each collection scheme to obtain a time series. Based on the time series, the computer device determines the target collection scheme from the multiple collection schemes, and determines the target collection category of each formula parameter based on the target collection scheme. The computer device obtains multiple data extraction formulas and target collection categories corresponding to each formula parameter, and the computer device uses the formula parameter belonging to the non-collection category as the first formula parameter, and determines the first sub-parameter corresponding to the first formula parameter based on the multiple first formula parameters. The computer device divides multiple data extraction formulas including the same first sub-parameter into non-collection sets corresponding to the corresponding first sub-parameters based on each first sub-parameter. For each non-collection set, the computer device determines whether there is a second formula parameter belonging to the custom collection category in the data extraction formula in the corresponding non-collection set. If the second formula parameter exists in the data extraction formula in the non-collection set, the computer device obtains the first sub-parameter and the second formula parameter, and for each second formula parameter, the corresponding second formula parameter is combined with each first sub-parameter to obtain a first parameter combination. The computer device combines each second formula parameter to obtain a second parameter combination. The computer device uses the first parameter combination and the second parameter combination as the first data extraction combination. For each first data acquisition combination, the computer device determines whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional collection category based on the data acquisition formula in the corresponding data acquisition combination. If there are at least two third formula parameters with different third sub-parameters in the third formula parameter belonging to the conditional collection category based on the data acquisition formula in the corresponding first data acquisition combination, the third formula parameter with at least two different third sub-parameters is used as the second condition parameter. The third sub-parameters in each second condition parameter are combined to obtain multiple second condition combinations, and each second condition combination is combined with the formula parameter in the first data acquisition combination to obtain a third data acquisition combination.For example, the third data acquisition combination can be that all accounts in the 2018.1 period do not contain accounting dimension data, and the corresponding data acquisition formulas are formula 1 and formula 2; the third data acquisition combination can be that accounts 1121 in the 2018.1 period acquire customer accounting dimension data, and the corresponding data acquisition formulas are formula 3 and formula 4; the third data acquisition combination can be that accounts 4104 data are acquired in the 2020.12 period, corresponding to formula N. Based on the formula parameters included in the third data acquisition combination, the corresponding classification data acquisition conditions are determined. For each classification data acquisition condition, the computer device determines the target data packet corresponding to the corresponding classification data acquisition condition and the target data acquisition formula. For each classification data acquisition formula, the computer device acquires data from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula, and obtains the business data corresponding to each target data acquisition formula. For example, formula 1 and formula 2 both acquire data from data packet 1, formula 3 and formula 4 both acquire data from data packet 2, and formula N acquires data from data packet N.

[0116] In this embodiment, multiple data acquisition formulas corresponding to the data acquisition request are obtained; wherein each data acquisition formula includes multiple formula parameters. The target collection categories corresponding to each formula parameter are obtained, and based on the target collection categories corresponding to each formula parameter, the multiple formula parameters are collected and combined to obtain multiple classified data acquisition conditions. In this way, in combination with the target collection categories corresponding to each formula parameter, the formula parameters can be collected and combined more specifically to obtain more accurate classified data acquisition conditions. From the multiple data acquisition formulas, at least one target data acquisition formula corresponding to each classified data acquisition condition is obtained. In this way, each data acquisition formula can be accurately divided into the adapted data acquisition conditions, so that each data acquisition condition contains the corresponding target data acquisition formula. Based on the formula parameters included in each classified data acquisition condition, the target data packet corresponding to the corresponding classified data acquisition condition is determined. In this way, for each classified data acquisition condition, the target data packet corresponding to the corresponding data acquisition condition can be adapted to the target data acquisition formula, so that each target data packet can accurately determine the corresponding target data acquisition formula. For each classification data acquisition condition, data is acquired from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula, and the business data corresponding to each target data acquisition formula is obtained; the acquired business data is used to form a business report. In this way, based on the target data acquisition formula corresponding to each target data packet, the corresponding business data can be acquired quickly and accurately. Compared with the traditional large data packet acquisition, the present invention can obtain more accurate classification data acquisition conditions based on the target collection category corresponding to each formula parameter. In this way, a target data packet with a more accurate and detailed acquisition range can be obtained based on the classification data acquisition conditions. Therefore, each data acquisition formula is parsed and acquired from the corresponding target data packet with a small data volume, which can greatly improve the data acquisition accuracy while ensuring the reduction of data acquisition time, thereby greatly improving the data acquisition efficiency of the business report. In addition, in the traditional process of acquiring data packets, due to too much business detail data, a large amount of data is acquired from the database, and there is a serious problem of resource consumption of database interface, server memory, processor, etc. This application determines the target data packet through each classification data acquisition condition, that is, through the formula parameters actually contained in each classification data acquisition condition, the target data packet is determined in a targeted manner according to needs, which can achieve on-demand data acquisition and reduce the consumption of resources such as memory, database, and network transmission, that is, it can avoid the serious resource consumption problem caused by the traditional data packet acquisition process. In addition, hot plugging can be achieved through plug-in registration and hot start methods, and it can be used in different customer data scenarios, thereby achieving interventionability. In addition, according to the resource consumption situation, combined with the big data model algorithm, an intelligent data acquisition solution can be generated and used, and the collection logic can be continuously optimized.

[0117] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0118] Based on the same inventive concept, the embodiment of the present application also provides a device for obtaining data from a business report for implementing the method for obtaining data from the business report involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the device for obtaining data from one or more business reports provided below can be referred to the limitations on the method for obtaining data from the business report above, and will not be repeated here.

[0119] In one embodiment, Figure 6 As shown, a data acquisition device for a business report is provided, comprising: a first acquisition module 602, an acquisition module 604, a second acquisition module 606, a determination module 608 and a data acquisition module 610, wherein:

[0120] The first acquisition module 602 is used to acquire multiple data acquisition formulas corresponding to the data acquisition request; wherein each data acquisition formula includes multiple formula parameters.

[0121] The obtaining module 604 is used to obtain the target collection category corresponding to each formula parameter, and based on the target collection category corresponding to each formula parameter, combine multiple formula parameters to obtain multiple classification data collection conditions.

[0122] The second acquisition module 606 is used to acquire at least one target data acquisition formula corresponding to each classification data acquisition condition from the multiple data acquisition formulas.

[0123] The determination module 608 is used to determine the target data packet corresponding to the corresponding classification data acquisition condition based on the formula parameters included in each classification data acquisition condition.

[0124] The data acquisition module 610 is used to acquire data from the target data packet corresponding to each classification data acquisition condition according to the corresponding target data acquisition formula, and obtain the business data corresponding to each target data acquisition formula; the acquired business data is used to form a business report.

[0125] In one embodiment, the acquisition module 604 is further used to combine multiple formula parameters and multiple collection categories to obtain multiple collection schemes; wherein each formula parameter in each collection scheme corresponds to a collection category. For each collection scheme, data is obtained through the corresponding collection scheme, and the data collection time of the corresponding collection scheme is counted. Based on the data collection time corresponding to each collection scheme, a target collection scheme is determined from multiple collection schemes; wherein the target collection scheme contains target collection categories corresponding to each formula parameter.

[0126] In one embodiment, the acquisition module 604 is used to determine the first sub-parameter in the first formula parameter belonging to the non-collection category based on multiple number-collecting formulas and the target collection category corresponding to each formula parameter. Based on multiple number-collecting formulas including the same first sub-parameter, determine the non-collection set corresponding to the corresponding first sub-parameter. For each non-collection set, determine whether there is a second formula parameter belonging to the custom collection category in the number-collecting formula in the corresponding non-collection set. If the second formula parameter exists in the number-collecting formula in the non-collection set, then determine at least one first number-collecting combination based on the first sub-parameter and the second formula parameter. For each first number-collecting combination, based on the number-collecting formula in the corresponding first number-collecting combination, determine whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional collection category. If not, then directly determine the corresponding classification number-collecting condition based on each formula parameter included in the first number-collecting combination.

[0127] In one embodiment, the acquisition module 604 is used to combine each second formula parameter with the corresponding first sub-parameter to obtain a first parameter combination; combine the second formula parameters to obtain multiple custom combinations, and combine each custom combination with the corresponding first sub-parameter to obtain a second parameter combination; and use the first parameter combination and the second parameter combination as a first data acquisition combination.

[0128] In one embodiment, the acquisition module 604 is also used to determine whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional collection category based on the number-collecting formula in the corresponding non-collection set if the second formula parameter does not exist in the number-collecting formula in the non-collection set. If so, the third formula parameter with at least two different third sub-parameters is used as the first condition parameter, and each third sub-parameter in each first condition parameter is combined to obtain multiple first condition combinations, and each first condition combination is combined with the first sub-parameter to obtain a second number-collecting combination. Based on each formula parameter included in the second number-collecting combination, the corresponding classification number-collecting condition is determined.

[0129] In one embodiment, the acquisition module 604 is also used for, if there are at least two third formula parameters with different third sub-parameters in the third formula parameters belonging to the conditional collection category based on the data collection formula in the corresponding first data collection combination, then the third formula parameters with at least two different third sub-parameters are used as the second condition parameter. The third sub-parameters in each second condition parameter are combined to obtain multiple second condition combinations, and each second condition combination is combined with the formula parameters in the first data collection combination to obtain a third data collection combination. Based on the formula parameters included in the third data collection combination, the corresponding classification data collection condition is determined.

[0130] Each module in the data acquisition device of the above business report can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0131] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data for obtaining business reports. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for obtaining data for a business report is implemented.

[0132] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0133] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0134] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0135] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0138] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for obtaining data from a business report, characterized in that: The method comprises: Acquire multiple data acquisition formulas corresponding to the data acquisition request; wherein each data acquisition formula includes multiple formula parameters; Obtaining target collection categories corresponding to the respective formula parameters, determining a first sub-parameter in a first formula parameter belonging to a non-collection category based on a plurality of data acquisition formulas and the target collection categories corresponding to the respective formula parameters, and determining a non-collection set corresponding to the corresponding first sub-parameter based on a plurality of data acquisition formulas including the same first sub-parameter; For each non-aggregation set, if there is a second formula parameter belonging to the custom aggregation category in the data extraction formula in the non-aggregation set, at least one first data extraction combination is determined based on the first sub-parameter and the second formula parameter, and for each first data extraction combination, based on the data extraction formula in the corresponding first data extraction combination, it is determined whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional aggregation category; If it does not exist, directly determine the corresponding classification data acquisition conditions based on the formula parameters included in the first data acquisition combination; From the plurality of data acquisition formulas, obtaining at least one target data acquisition formula corresponding to each classification data acquisition condition; Based on the formula parameters included in each classification data acquisition condition, determine the target data packet corresponding to the corresponding classification data acquisition condition; For each classification data acquisition condition, data is acquired from the target data packet corresponding to the corresponding classification data acquisition condition according to the corresponding target data acquisition formula to obtain business data corresponding to each target data acquisition formula; the acquired business data is used to form a business report.

2. The method according to claim 1, characterized in that Before obtaining the target collection categories corresponding to the respective formula parameters, the method further includes: Combining multiple formula parameters and multiple collection categories respectively to obtain multiple collection schemes; wherein each formula parameter in each collection scheme corresponds to one collection category; For each collection scheme, data is collected through the corresponding collection scheme, and the data collection time of the corresponding collection scheme is counted; Based on the data acquisition time corresponding to each collection scheme, a target collection scheme is determined from multiple collection schemes; wherein the target collection scheme contains target collection categories corresponding to each formula parameter.

3. The method according to claim 1, characterized in that The determining at least one first number-taking combination based on the first sub-parameter and the second formula parameter includes: Combining each second formula parameter with the corresponding first sub-parameter to obtain a first parameter combination; Combining the second formula parameters to obtain a plurality of custom combinations, and combining each custom combination with a corresponding first sub-parameter to obtain a second parameter combination; The first parameter combination and the second parameter combination are used as a first data acquisition combination.

4. The method according to claim 1, characterized in that: The method further comprises: If the second formula parameter does not exist in the number-taking formula in the non-aggregation set, judging whether there are at least two different third sub-parameters in any third formula parameter belonging to the conditional aggregation category based on the number-taking formula in the corresponding non-aggregation set; If so, a third formula parameter having at least two different third sub-parameters is used as a first condition parameter, and each third sub-parameter in each first condition parameter is combined to obtain a plurality of first condition combinations, and each first condition combination is combined with the first sub-parameter to obtain a second number extraction combination; Based on the formula parameters included in the second data acquisition combination, the corresponding classification data acquisition conditions are determined.

5. The method according to claim 1, characterized in that The method further comprises: If, based on the data extraction formula in the corresponding first data extraction combination, there are at least two third formula parameters with different third sub-parameters in the third formula parameters belonging to the conditional aggregation category, the third formula parameters with at least two different third sub-parameters are used as the second conditional parameters; Combining the third sub-parameters in the second condition parameters to obtain a plurality of second condition combinations, and combining each second condition combination with the formula parameter in the first number-taking combination to obtain a third number-taking combination; Based on the formula parameters included in the third data acquisition combination, the corresponding classification data acquisition conditions are determined.

6. A data acquisition device for business reports, characterized in that: The device comprises: A first acquisition module is used to acquire multiple data acquisition formulas corresponding to the data acquisition request; wherein each data acquisition formula includes multiple formula parameters; An acquisition module is used to obtain target collection categories corresponding to each formula parameter, determine a first sub-parameter in a first formula parameter belonging to a non-collection category based on multiple number-collection formulas and target collection categories corresponding to each formula parameter, and determine a non-collection set corresponding to the corresponding first sub-parameter based on multiple number-collection formulas including the same first sub-parameter; for each non-collection set, if there is a second formula parameter belonging to a custom collection category in the number-collection formula in the non-collection set, determine at least one first number-collection combination based on the first sub-parameter and the second formula parameter, and for each first number-collection combination, determine whether there are at least two different third sub-parameters in any third formula parameter belonging to a conditional collection category based on the number-collection formula in the corresponding first number-collection combination; if not, directly determine the corresponding classification number-collection condition based on each formula parameter included in the first number-collection combination; A second acquisition module is used to acquire at least one target data acquisition formula corresponding to each classification data acquisition condition from the multiple data acquisition formulas; A determination module, used to determine a target data packet corresponding to a corresponding classification data acquisition condition based on a formula parameter included in each classification data acquisition condition; The data acquisition module is used to acquire data from the target data packet corresponding to each classification data acquisition condition according to the corresponding target data acquisition formula for each classification data acquisition condition, and obtain the business data corresponding to each target data acquisition formula; the acquired business data is used to form a business report.

7. The device according to claim 6, characterized in that The obtaining module is further used to combine multiple formula parameters and multiple collection categories respectively to obtain multiple collection schemes; wherein each formula parameter in each collection scheme corresponds to a collection category; for each collection scheme, data is obtained through the corresponding collection scheme, and the data obtaining time of the corresponding collection scheme is counted; based on the data obtaining time corresponding to each collection scheme, a target collection scheme is determined from the multiple collection schemes; wherein the target collection scheme contains the target collection categories corresponding to each formula parameter.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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