Method and apparatus for extracting business data

Abstract

The application provides a service data extraction method and device, relates to the technical field of financial science and technology and information technology industry, and comprises the following steps: obtaining multiple platform levels based on the average service amount of service data generated by each service platform in a preset time interval, each platform level comprising multiple service platforms; for any platform level comprising multiple service platforms, establishing an inspection extraction relationship between the multiple service platforms in the platform level; for any platform level, performing service data extraction on each service platform according to a preset service extraction rule based on the inspection extraction relationship to obtain target service data, wherein the target service data extracted from each service platform is used for detection by the service platforms having the inspection extraction relationship. Therefore, the target service data is uniformly and randomly covered by performing extraction on the multiple service platforms in each platform level according to the preset service extraction rule, and the reliability of the service data is improved.

Description

Technical Field

[0001] This invention relates to the fields of financial technology and information technology industries, and in particular to a method and apparatus for extracting business data. Background Technology

[0002] Currently, the extraction of business data is still determined subjectively by humans, which carries risks such as lack of intelligence, unclear and inconsistent standards, and even the possibility of manipulation. Specifically, taking the extraction of business data in banks as an example, the size of staff and the amount of business data in different branches may vary significantly. If completely random sampling is used without differentiation, it may lead to an uneven workload in the audit process, resulting in low work efficiency. Some banks may have the system automatically generate extraction results for business data, but without certain business rules, they use a completely random method. This may result in the sampled business data being concentrated on the same type of business product or the same entity user, making the sampled data results unrepresentative. Therefore, there is an urgent need for a more reliable method for extracting business data. Summary of the Invention

[0003] The present invention aims to at least partially solve one of the technical problems in the related art.

[0004] Therefore, the first objective of this invention is to propose a method for extracting business data, which extracts data from multiple business platforms at various platform levels according to preset business extraction rules, thereby achieving uniform and random coverage of target business data and improving the reliability of business data.

[0005] The second objective of this invention is to provide a business data extraction device.

[0006] The third objective of this invention is to provide an electronic device.

[0007] The fourth objective of this invention is to provide a non-transitory computer-readable storage medium storing computer instructions.

[0008] The fifth objective of this invention is to provide a computer program product.

[0009] To achieve the above objectives, a first aspect of the present invention provides a method for extracting business data, comprising:

[0010] Based on the average business volume of business data generated by each business platform within a preset time interval, multiple platform levels are obtained, with each platform level including multiple business platforms.

[0011] For any platform level that includes multiple business platforms, establish the inspection and extraction relationship among the multiple business platforms at the platform level;

[0012] For multiple business platforms at any of the platform levels, based on the inspection and extraction relationship, business data is extracted from each of the business platforms according to the preset business extraction rules to obtain target business data. The target business data extracted from each of the business platforms is used for detection on the business platforms with the inspection and extraction relationship.

[0013] To achieve the above objectives, a second aspect of the present invention provides a business data extraction apparatus, comprising:

[0014] The hierarchical module is used to classify multiple platform levels based on the average business volume of business data generated by each business platform within a preset time interval. Each platform level includes multiple business platforms.

[0015] A module is established to establish the inspection and extraction relationship between the multiple business platforms at any platform level that includes multiple business platforms.

[0016] An extraction module is used to extract business data from multiple business platforms at any platform level according to the inspection extraction relationship and a preset business extraction rule to obtain target business data. The target business data extracted from each business platform is used for detection on the business platforms with the inspection extraction relationship.

[0017] To achieve the above objectives, a third aspect of the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0018] To achieve the above objectives, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method described in the first aspect.

[0019] To achieve the above objectives, a fifth aspect of the present invention provides a computer program product that, when executed by a processor, implements the method described in the first aspect.

[0020] The business data configuration method, apparatus, electronic device, and storage medium provided in this invention, based on the average business volume of business data generated by each business platform within a preset time interval, classifies into multiple platform levels, each platform level including multiple business platforms. For any platform level including multiple business platforms, an inspection and extraction relationship is established between the multiple business platforms within the platform level. For any platform level, based on the inspection and extraction relationship, business data is extracted from each business platform according to a preset business extraction rule to obtain target business data. The target business data extracted from each business platform is used for detection on the business platforms with the inspection and extraction relationship. Thus, by extracting from multiple business platforms in each platform level according to the preset business extraction rule, uniform and random coverage of target business data is achieved, improving the reliability of business data.

[0021] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0023] Figure 1 A flowchart illustrating a method for extracting business data provided in an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of a process for establishing an extraction relationship, provided in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of a process for generating target business data based on business extraction rules, provided by an embodiment of the present invention.

[0026] Figure 4 A flowchart illustrating another method for extracting business data provided in an embodiment of the present invention;

[0027] Figure 5 This is a hierarchical example diagram of a business platform provided in an embodiment of the present invention;

[0028] Figure 6 This is a schematic diagram of a business data extraction device provided in an embodiment of the present invention. Detailed Implementation

[0029] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0030] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this invention all comply with the relevant provisions of national laws and regulations.

[0031] The method and apparatus for extracting business data according to embodiments of the present invention are described below with reference to the accompanying drawings.

[0032] Figure 1 This is a flowchart illustrating a method for extracting business data provided in an embodiment of the present invention.

[0033] like Figure 1 As shown, the method includes the following steps:

[0034] Step 101: Based on the average business volume of business data generated by each business platform within a preset time interval, multiple platform levels are obtained, where each platform level includes multiple business platforms.

[0035] Optionally, the business platform may be a bank, but it is not limited to this, and this embodiment does not specifically limit it.

[0036] Optionally, when each business platform is a different bank, the business data generated by each business platform within a preset time interval can be data from the public credit sector, specifically data on financial products purchased by physical users, but not limited to this.

[0037] The preset time interval can be any time period. Specifically, it can be a quarter, but it is not limited to this. This embodiment does not make any specific limitation on this.

[0038] In some embodiments, to ensure the balance of each platform level, multiple platform levels can be obtained by classifying the average business volume of business data generated by each business platform in a preset time interval, thereby achieving balanced classification of each business platform and facilitating later use.

[0039] Step 102: For any platform level that includes multiple business platforms, establish the inspection extraction relationship between the multiple business platforms at the platform level.

[0040] In some embodiments, the extraction relationship can be determined as follows: for any platform level that includes multiple business platforms, the multiple business platforms are sorted to obtain a platform sequence; it is determined that there is an extraction relationship between a first business platform and an adjacent second business platform, wherein the first business platform is the extracted business platform and the second business platform is the extraction business platform corresponding to the first business platform; it is determined that there is an extraction relationship between the last business platform in the platform sequence and the first business platform, wherein the last business platform is the extracted platform and the first business platform is the extraction business platform corresponding to the last business platform.

[0041] Specifically, taking a bank as an example, such as a business platform... Figure 2As shown, based on the average business volume of the business data generated by each business platform in a preset time interval, multiple platform levels are hierarchically obtained (taking 4 platform levels as an example), and a two-dimensional array level[4][] of sub - level classification of any platform level is selected. The [] stores the row numbers of the sub - branches. Data adjustment is performed on the two - dimensional array level[4][] of sub - level classification of the sub - branches, and a new adjustLevel[][] is created. Traverse the level array (the level array starts from the first platform level serial number i = 0, and the adjustLevel array serial number j = 0). Specifically, when i < 4, judge whether the number of sub - branches size() in the level[i] platform level <= 1 holds. If it does not hold, put the data of the level[i] platform level into the adjustLevel[j++] platform level. If it holds, judge whether it is the last platform level (i == 3). When it is not the last platform level, put the data of the level[i] platform level into the adjustLevel[j] platform level, and judge whether the number of sub - branches in adjustLevel[j] is greater than 1 (if greater than 1, put the data of the level[i] platform level into the adjustLevel[j++] platform level; if less than 1, repeat the above steps to continue judging the number of sub - branches in the level[i] platform level). When it is the last platform level, put the data of the level[i] platform level into the adjustLevel[j - 1] platform level. Thus, if it is not the last platform level and there is only 1 sub - branch within the platform level, it is classified into the sub - branches of the next platform level for matching (e.g., the first platform level is classified into the second platform level, and so on); if it is the last platform level and there is only 1 sub - branch, it is classified upward into the previous platform level for matching; finally, the adjusted two - dimensional array adjustLevel[][] is obtained. The adjusted platform levels are less than or equal to 4, and the number of sub - branches in each platform level must be greater than 1. Then traverse the adjusted two - dimensional array adjustLevel[][] (and traverse the platform level serial number starting from k = 0), and judge whether k < the size of the first platform level of adjustLevel holds. If it holds, shuffle the row number sequence of the sub - branches in the k - th platform level, for example, obtain an order like row C, row D, row B, row A, and establish a matching relationship (the sub - branch in the previous position of the sequence is used as the inspected row, and the adjacent next - position sub - branch is used as the inspection row. For example, for CDBA, the arrow - directed one is the inspected row), and the sub - branch in the last position is inspected by the sub - branch in the first (the first) position. For example, for AC, the arrow - directed one is the inspected row).

[0042] Step 103: For multiple business platforms at any platform level, based on the inspection and extraction relationship, business data is extracted from each business platform according to the preset business extraction rules to obtain target business data. The target business data extracted from each business platform is used for detection on business platforms with inspection and extraction relationships.

[0043] In some embodiments, one way to determine the preset business extraction rules is to obtain the total amount of business data generated by each business platform in a preset time interval, the preset business data extraction ratio, and the business extraction type of the business data, and determine the business extraction rules based on the total amount of business data, the business data extraction ratio, and the business extraction type.

[0044] In other embodiments, one approach to extracting target business data based on preset business extraction rules can be as follows: Figure 3As shown, specifically, taking a bank as an example, the first step is to set business extraction rules, including: based on the bank's actual user needs, setting the corresponding business data time range, business data extraction ratio, total extraction limit, and business extraction type (major category or specific salable business products; each business extraction type can also specify the extraction ratio and extraction limit for each business product). This determines the business extraction rules. Then, the total business data volume within the business time range is obtained. Based on the business extraction rules, the maximum extraction limit for the total business data volume is calculated as min(total extraction limit, total business data volume * business data extraction ratio), and the maximum extraction limit for each business product is calculated as min(extraction limit for each business product, total business data volume for each business product * extraction ratio for each business product). The extraction rules are then validated to ensure that the total extraction limit for each business product is less than or equal to the total extraction limit for the business data. If this is not met, the business data extraction rules need to be adjusted. Take the ratio and / or the upper limit of the total number of samples until the verification conditions are met; calculate the average number of samples per month for each business product (average number of samples per month = number of samples / month span of the business time interval), and initialize an entity user ID hash table clientHashMap to store the number of times the entity user ID has been selected. Then, traverse each business product and check if the traversal sequence number < number of business products is true. If not, exit the traversal loop and return the target business data. If true, retrieve the sampled data sampleListByMonth (sampled data information tuple < entity user ID, business product ID>) that meets the conditions in each month through the uniqueness query of the entity user ID. If the number of sampled data is less than the average number of samples per month, traverse the sampled data list sampleListByMonth and check the number of times the entity user ID of each sampled data has been selected in the hash table clientHashMap.If the value is 0, a tuple with a value of 1 (Entity User ID, 1) is inserted into the corresponding hash table, and this extracted data is added to the target list targetList. If the value is greater than 0 and less than 2, the value in the corresponding hash table is incremented by 1, and this extracted data is added to the target list targetList. In addition, if the number of extracted data is greater than the monthly average number of extracted data, a corresponding number of business data are randomly extracted (the number of extracted data is less than or equal to the monthly average number of extracted data). The number of times the entity user ID of the selected business data is selected is determined in the hash table clientHashMap. If the value is 0, a tuple with a value of 1 (Entity User ID, 1) is inserted into the corresponding hash table, and this extracted data is added to the target list targetList. If the value is greater than 0 and less than 2, the value in the corresponding hash table is incremented by 1, and this extracted data is added to the target list targetList. At this time, the data in the target list targetList is the target business data. Therefore, based on the established extraction rules, random sampling of the business data to be checked is performed to ensure that the amount of business data extracted each month is as even as possible, rather than concentrated in a few months. Furthermore, the selected entity users are also distributed as randomly and evenly as possible, with each entity user having no more than two entries extracted, in order to achieve the most even coverage of the sampled business products.

[0045] The business data extraction method of this invention, based on the average business volume of business data generated by each business platform within a preset time interval, divides the data into multiple platform levels, each platform level including multiple business platforms. For any platform level including multiple business platforms, an inspection and extraction relationship is established between the multiple business platforms within the platform level. For any platform level, based on the inspection and extraction relationship, business data is extracted from each business platform according to a preset business extraction rule to obtain target business data. The target business data extracted from each business platform is used for detection on the business platforms with inspection and extraction relationships. Thus, by extracting data from multiple business platforms in each platform level according to the preset business extraction rule, uniform and random coverage of target business data is achieved, improving the reliability of business data.

[0046] In summary, to better view the extraction of business data, the extraction relationship and target business data can be displayed on the corresponding business terminal of the business platform, and individual target business data can be fine-tuned and re-extracted and replaced in a completely random manner, which facilitates the subsequent maintenance and use of target business data.

[0047] To clearly illustrate the previous embodiment, this embodiment also provides a method for extracting business data. Figure 4This is a flowchart illustrating another method for extracting business data provided in an embodiment of the present invention.

[0048] like Figure 4 As shown, the method may include the following steps:

[0049] Step 401: Determine the average business volume of each business platform based on the business data generated by each business platform within the preset time interval.

[0050] In some embodiments, when the business platform is a bank, one way to determine the average business volume of each business platform based on the business data generated by each business platform within a preset time interval is: Average business volume = Business volume within the preset time interval / Number of business personnel in the branch.

[0051] Understandably, to better understand this application, the average business volume can also be expressed as the average business volume per person in each branch.

[0052] Step 402: Classify the business platforms that generate business data based on average business volume to obtain multiple candidate platform levels.

[0053] In some embodiments, when the business platform is a bank, the business platform (bank) can be classified according to the average business volume of each branch to obtain the platform level of each bank, such as... Figure 5 As shown, specifically, an average business volume >= 800 can be designated as the first-level business platform (L1), 500 < average business volume < 800 as the second-level business platform (L2), 200 < average business volume <= 500 as the third-level business platform (L3), and an average business volume <= 200 as the fourth-level business platform (L4).

[0054] Step 403: If there is a target candidate platform level that includes only one business platform among multiple candidate platform levels, then according to the merging rules, the business platforms included in the target candidate business platform are merged into other candidate platform levels to obtain multiple merged platform levels.

[0055] In some embodiments, the merging rules include: if the target candidate platform level is not the last level among multiple platform levels, then the business platforms under the target candidate platform level are merged into the next level platform level; if the target candidate platform level is the last level among multiple platform levels, then the business platforms under the target candidate platform level are merged into the previous level platform level.

[0056] Step 404: For any platform level that includes multiple business platforms, establish the inspection extraction relationship between the multiple business platforms at the platform level.

[0057] Step 405: For multiple business platforms at any platform level, based on the inspection extraction relationship, business data is extracted from each business platform according to the preset business extraction rules to obtain target business data. The target business data extracted from each business platform is used for detection on business platforms with inspection extraction relationships.

[0058] It should be noted that the specific implementation methods of steps 404 to 405 can be found in the relevant descriptions in the above embodiments.

[0059] The business data extraction method of this invention determines the average business volume of each business platform based on the business data generated by each business platform within a preset time interval. Based on the average business volume, the business platforms generating business data are classified into multiple candidate platform levels. If a target candidate platform level exists that includes only one business platform, the business platforms included in the target candidate platform are merged into other candidate platform levels according to merging rules, resulting in multiple merged platform levels. For any platform level including multiple business platforms, an inspection and extraction relationship is established between the multiple business platforms within that platform level. For any platform level, based on the inspection and extraction relationship, business data is extracted from each business platform according to preset business extraction rules to obtain target business data. The target business data extracted from each business platform is used for detection on business platforms with inspection and extraction relationships. Therefore, by extracting data from multiple business platforms within each platform level according to preset business extraction rules, the balance of the target business data is ensured, and the representativeness of the extracted target business data is guaranteed.

[0060] To implement the above embodiments, the present invention also proposes a business data extraction device.

[0061] Figure 6 This is a schematic diagram of a business data extraction device provided in an embodiment of the present invention.

[0062] like Figure 6 As shown, the data extraction device 60 includes: a hierarchical module 61, an establishment module 62, and an extraction module 63.

[0063] The grading module 61 is used to grade multiple platform levels based on the average business volume of business data generated by each business platform within a preset time interval, wherein each platform level includes multiple business platforms.

[0064] Module 62 is used to establish the inspection and extraction relationship between the multiple business platforms in any platform level that includes multiple business platforms.

[0065] The extraction module 63 is used to extract business data from multiple business platforms at any platform level according to the inspection extraction relationship and a preset business extraction rule to obtain target business data. The target business data extracted from each business platform is used to detect the business platforms with the inspection extraction relationship.

[0066] Furthermore, in one possible implementation of this invention, the hierarchical module 61 is specifically used for:

[0067] The average business volume of each business platform is determined based on the business data generated by each business platform within a preset time interval.

[0068] The business platforms that generate the business data are classified based on the average business volume to obtain multiple candidate platform levels;

[0069] If there is a target candidate platform level among the multiple candidate platform levels that includes only one business platform, then according to the merging rules, the business platforms included in the target candidate business platform are merged into other candidate platform levels to obtain multiple merged platform levels.

[0070] Furthermore, in one possible implementation of this invention, the merging rule includes:

[0071] If the target candidate platform level is not the last level among the multiple platform levels, then the business platforms under the target candidate platform level will be merged into the next lower platform level.

[0072] If the target candidate platform level is the last among the multiple platform levels, then the business platforms under the target candidate platform level will be merged into the platform level of the next higher level.

[0073] Furthermore, in one possible implementation of this invention, the extraction relationship is determined in the following manner:

[0074] For any platform level that includes multiple business platforms, sort the multiple business platforms to obtain a platform sequence;

[0075] It is determined that there is an extraction relationship between the first business platform and the adjacent second business platform, wherein the first business platform is the extracted business platform and the second business platform is the extraction business platform corresponding to the first business platform.

[0076] It is determined that the last business platform in the platform sequence has the inspection and extraction relationship with the first business platform, wherein the last business platform is the extracted platform and the first business platform is the extraction business platform corresponding to the last business platform.

[0077] Furthermore, in one possible implementation of this invention, the service extraction rules include:

[0078] Obtain the total amount of business data generated by each business platform within a preset time interval, as well as the preset business data extraction ratio and the business data extraction type;

[0079] The business extraction rules are determined based on the total business data volume, the business data extraction ratio, and the business extraction type.

[0080] Based on the above embodiments, this invention also provides a possible implementation of a business data extraction device. In addition to the previous embodiment, the device further includes:

[0081] The display module is used to display the inspection extraction relationship and the target business data on the business terminal corresponding to the business platform.

[0082] The business data extraction device of this invention, based on the average business volume of business data generated by each business platform within a preset time interval, classifies the data into multiple platform levels, each platform level including multiple business platforms. For any platform level including multiple business platforms, an inspection and extraction relationship is established between the multiple business platforms within the platform level. For any platform level, based on the inspection and extraction relationship, business data is extracted from each business platform according to a preset business extraction rule to obtain target business data. The target business data extracted from each business platform is used for detection on the business platforms with the inspection and extraction relationship. Thus, by extracting data from multiple business platforms within each platform level according to the preset business extraction rule, uniform and random coverage of the target business data is achieved, improving the reliability of the business data. To implement the above embodiments, this invention also proposes an electronic device, including:

[0083] At least one processor; and

[0084] A memory communicatively connected to the at least one processor; wherein,

[0085] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the aforementioned method.

[0086] To implement the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the aforementioned method.

[0087] To implement the above embodiments, the present invention also proposes a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0088] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0089] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0090] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0091] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0092] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0093] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0094] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0095] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for extracting business data, characterized in that, The method includes: Based on the average business volume of business data generated by each business platform within a preset time interval, multiple platform levels are obtained, with each platform level including multiple business platforms. For any platform level that includes multiple business platforms, establish the inspection and extraction relationship among the multiple business platforms at the platform level; For multiple business platforms at any of the platform levels, based on the inspection and extraction relationship, business data is extracted from each of the business platforms according to the preset business extraction rules to obtain target business data. The target business data extracted from each of the business platforms is used for detection on the business platforms with the inspection and extraction relationship. The inspection extraction relationship is determined in the following way: For any platform level that includes multiple business platforms, sort the multiple business platforms to obtain a platform sequence; It is determined that there is an inspection extraction relationship between the first business platform and the adjacent second business platform, wherein the first business platform is the extracted business platform and the second business platform is the extraction business platform corresponding to the first business platform. It is determined that the last business platform in the platform sequence has the inspection and extraction relationship with the first business platform, wherein the last business platform is the extracted platform and the first business platform is the extraction business platform corresponding to the last business platform; The business extraction rules include: Obtain the total amount of business data generated by each business platform within a preset time interval, as well as the preset business data extraction ratio and the business data extraction type; The business extraction rules are determined based on the total business data volume, the business data extraction ratio, and the business extraction type.

2. The method according to claim 1, characterized in that, The average business volume based on the business data generated by each business platform within a preset time interval is used to classify multiple platform levels, where each platform level includes multiple business platforms, including: The average business volume of each business platform is determined based on the business data generated by each business platform within a preset time interval. The business platforms that generate the business data are classified based on the average business volume to obtain multiple candidate platform levels; If there is a target candidate platform level among the multiple candidate platform levels that includes only one business platform, then according to the merging rules, the business platform included in the target candidate platform level is merged into other candidate platform levels to obtain multiple merged platform levels.

3. The method according to claim 2, wherein the merging rules include: If the target candidate platform level is not the last level among the multiple platform levels, then the business platforms under the target candidate platform level will be merged into the next lower platform level. If the target candidate platform level is the last among the multiple platform levels, then the business platforms under the target candidate platform level will be merged into the platform level of the next higher level.

4. The method according to claim 1, characterized in that, The method further includes: The inspection extraction relationship and the target business data are displayed on the business terminal corresponding to the business platform.

5. A business data extraction device, characterized in that, The device includes: The hierarchical module is used to classify multiple platform levels based on the average business volume of business data generated by each business platform within a preset time interval. Each platform level includes multiple business platforms. A module is established to establish the inspection and extraction relationship between the multiple business platforms at any platform level that includes multiple business platforms. An extraction module is used to extract business data from multiple business platforms at any platform level based on the inspection extraction relationship and according to preset business extraction rules to obtain target business data. The target business data extracted from each business platform is used to detect business platforms with the inspection extraction relationship. The inspection extraction relationship is determined in the following way: For any platform level that includes multiple business platforms, sort the multiple business platforms to obtain a platform sequence; It is determined that there is an inspection extraction relationship between the first business platform and the adjacent second business platform, wherein the first business platform is the extracted business platform and the second business platform is the extraction business platform corresponding to the first business platform. It is determined that the last business platform in the platform sequence has the inspection and extraction relationship with the first business platform, wherein the last business platform is the extracted platform and the first business platform is the extraction business platform corresponding to the last business platform; The business extraction rules include: Obtain the total amount of business data generated by each business platform within a preset time interval, as well as the preset business data extraction ratio and the business data extraction type; The business extraction rules are determined based on the total business data volume, the business data extraction ratio, and the business extraction type.

6. The apparatus according to claim 5, characterized in that, The hierarchical module is specifically used for: The average business volume of each business platform is determined based on the business data generated by each business platform within a preset time interval. The business platforms that generate the business data are classified based on the average business volume to obtain multiple candidate platform levels; If there is a target candidate platform level among the multiple candidate platform levels that includes only one business platform, then according to the merging rules, the business platform included in the target candidate platform level is merged into other candidate platform levels to obtain multiple merged platform levels.

7. The apparatus according to claim 6, wherein the merging rule includes: If the target candidate platform level is not the last level among the multiple platform levels, then the business platforms under the target candidate platform level will be merged into the next lower platform level. If the target candidate platform level is the last among the multiple platform levels, then the business platforms under the target candidate platform level will be merged into the platform level of the next higher level.

8. The apparatus according to claim 5, characterized in that, The device further includes: The display module is used to display the inspection extraction relationship and the target business data on the business terminal corresponding to the business platform.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-4.

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