Statistical result output method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a statistical result output method and device, electronic equipment and storage medium, which can be applied to the field of big data technology. The statistical result output method comprises: reading multiple pieces of labeled data from a database; reading a first structure label tree associated with a statistical business field from the database; associating the first structure label tree and the multiple pieces of labeled data based on the statistical business field, so as to determine multiple pieces of target data from the multiple pieces of labeled data; and performing statistical analysis on the multiple pieces of target data to output a statistical result.

Description

Technical Field

[0001] This disclosure relates to the field of big data technology, and specifically to a method, apparatus, device, medium, and program product for outputting statistical results. Background Technology

[0002] In various data statistical analysis scenarios, staff will generate a huge amount of business results data based on multiple different business segments during statistical work. In the next statistical work, historical business results data will often be consulted for reference. At the same time, when summarizing projects, it is necessary to classify and statistically analyze the results in order to better analyze business results data from a global perspective.

[0003] In the process of implementing this disclosure, it was found that due to the large amount of business results data and the fact that the business results are unstructured information, when classifying, managing and querying these unstructured business results, business personnel often need to spend a lot of energy to find the information they need from the large amount of business results accumulated over the years. Managers also find it difficult to statistically analyze the quantity and status of all business results from a global perspective, resulting in high labor costs, low work efficiency, and increased consumption of computer computing resources in the process of processing large amounts of data. Summary of the Invention

[0004] In view of the above problems, this disclosure provides a method, apparatus, device, medium and program product for outputting statistical results.

[0005] One aspect of this disclosure provides a method for outputting statistical results, including:

[0006] Read multiple sets of labeled data from the database;

[0007] Read the first structured label tree associated with the statistical business field from the database, where the statistical business field is used to characterize the scope of statistical business associated with performing statistical operations on multiple labeled data;

[0008] Based on statistical business fields, the first structure label tree is associated with multiple labeled data sets so as to identify multiple target data sets from the multiple labeled data sets.

[0009] Perform statistical analysis on multiple target data to output statistical results.

[0010] According to embodiments of this disclosure, each tagged data item is marked with an identification path label, and associating the first structured label tree and multiple tagged data items based on statistical business fields includes:

[0011] Multiple statistical path labels are generated based on statistical business fields and the first structure label tree;

[0012] The labeled data with the same identification path label and statistical path label are identified as the target data.

[0013] According to an embodiment of this disclosure, the statistical business field includes a multi-level statistical field, which is used to characterize multiple business levels involved in the statistical business scope. The first structure label tree includes multi-level node labels, which correspond to multiple business levels associated with the first structure label tree.

[0014] Multiple statistical path tags are generated based on statistical business fields and the first structured tag tree, including:

[0015] Based on the last-level statistical field in the statistical business fields, determine the first target node label corresponding to the last-level statistical field from the first structure label tree;

[0016] Using the first target node label as the path boundary, the multi-level parent node labels associated with the first target node label in the first structural label tree are taken as the parent path nodes of the path boundary, and the multi-level child node labels associated with the first target node label in the first structural label tree are taken as the child path nodes of the path boundary, so as to generate multiple statistical path labels.

[0017] According to embodiments of this disclosure, the above method further includes:

[0018] Read the second structured label tree associated with the labeling field from the database, where the labeling field is used to characterize the scope of the labeling business associated with performing the labeling operation on the data to be labeled;

[0019] Generate identification path labels based on the tagging fields and the second-structured tag tree;

[0020] After marking the data to be marked using the identification path label, the marked data is generated.

[0021] According to embodiments of this disclosure, generating identifier path labels based on the tagging field and the second structured tag tree includes:

[0022] Based on the tagging field, determine the second target node tag corresponding to the tagging business scope from the second structure tag tree;

[0023] Using the second target node label as the path terminator, the multi-level parent node labels associated with the second target node label in the second structure label tree are used as the parent path nodes of the path terminator to generate the identifier path label.

[0024] According to embodiments of this disclosure, wherein:

[0025] The database stores multiple structured label trees, each associated with a different business segment. Each structured label tree includes multi-level node labels, which in turn include root node labels and multi-level child node labels. The root node label corresponds to the business segment associated with the structured label tree, and the multi-level child node labels correspond to multiple business levels under the business segment associated with the structured label tree.

[0026] According to embodiments of this disclosure, the above method further includes:

[0027] Perform maintenance operations on the node labels in the structure label tree, including at least one of the following: adding node labels, modifying node label names, modifying the display order of node labels, moving node labels, merging node labels, disabling node labels, and enabling node labels.

[0028] According to embodiments of this disclosure, wherein:

[0029] In a multi-level node tag tree, multiple node tags at the same level that are associated with the same parent node tag have different names.

[0030] In the multi-level node tags in the structural tag tree, each node tag is either enabled or disabled. Specifically, the multi-level sub-node tags of a disabled node tag are disabled, and the multi-level parent node tags of an enabled node tag are enabled.

[0031] Another aspect of this disclosure provides a statistical result output device, including a first reading module, a second reading module, an association module, and a statistical module.

[0032] The first reading module is used to read multiple sets of tagged data from the database;

[0033] The second reading module is used to read the first structured label tree associated with the statistical business field from the database. The statistical business field is used to characterize the statistical business scope associated with performing statistical operations on multiple labeled data.

[0034] The association module is used to associate the first structure label tree with multiple labeled data based on statistical business fields, so as to identify multiple target data from multiple labeled data.

[0035] The statistics module is used to perform statistical analysis on multiple target data to output statistical results.

[0036] According to embodiments of this disclosure, each tagged data is marked with an identification path label, and the association module includes a first generation unit and a first determination unit.

[0037] The first generation unit is used to generate multiple statistical path labels based on statistical business fields and a first structure label tree; the first determination unit is used to determine the labeled data with the same identifier path label as the statistical path label as the target data.

[0038] According to embodiments of this disclosure, the statistical business field includes multi-level statistical fields, which are used to characterize multiple business levels involved in the statistical business scope. The first structural label tree includes multi-level node labels, which correspond to multiple business levels associated with the first structural label tree.

[0039] The first generation unit includes a determination subunit and a generation subunit.

[0040] The determination subunit is used to determine the first target node label corresponding to the last-level statistical field in the statistical business field from the first structure label tree; the generation subunit is used to take the first target node label as the path boundary point, take the multi-level parent node labels associated with the first target node label in the first structure label tree as the parent path node of the path boundary point, and take the multi-level child node labels associated with the first target node label in the first structure label tree as the child path node of the path boundary point, so as to generate multiple statistical path labels.

[0041] According to embodiments of this disclosure, the above-described apparatus further includes a third reading module, a generation module, and a marking module.

[0042] The third reading module is used to read the second structure tag tree associated with the tagging field from the database. The tagging field is used to represent the tagging business scope associated with the tagging operation performed on the data to be tagged. The generation module is used to generate identification path tags based on the tagging field and the second structure tag tree. The tagging module is used to generate tagged data after tagging the data to be tagged using the identification path tags.

[0043] According to embodiments of this disclosure, the generation module includes a second determining unit and a second generation unit.

[0044] The second determining unit is used to determine the second target node label corresponding to the labeling business scope from the second structure label tree based on the labeling field; the second generating unit is used to generate an identifier path label by taking the second target node label as the path end node and taking the multi-level parent node labels associated with the second target node label in the second structure label tree as the parent path nodes of the path end node.

[0045] According to embodiments of this disclosure, the database stores multiple structural tag trees, each of which is associated with a different business segment. Each structural tag tree includes multi-level node tags, which include root node tags and multi-level child node tags. The root node tags correspond to the business segments associated with the structural tag tree, and the multi-level child node tags correspond to multiple business levels under the business segments associated with the structural tag tree.

[0046] According to embodiments of this disclosure, the above-described apparatus further includes a maintenance module for performing maintenance operations on node labels in a structural label tree, wherein the maintenance operations include at least one of the following: adding node labels, modifying node label names, modifying the display order of node labels, moving node labels, merging node labels, disabling node labels, and enabling node labels.

[0047] According to embodiments of this disclosure, in the multi-level node tags in the structural tag tree, the names of multiple same-level node tags associated with the same parent node tag are different; in the multi-level node tags in the structural tag tree, the state of each node tag is enabled or disabled, wherein the state of the multi-level lower-level node tags of a disabled node tag is disabled, and the state of the multi-level parent node tags of an enabled node tag is enabled.

[0048] Another aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the above-described statistical result output method.

[0049] Another aspect of this disclosure provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the above-described statistical result output method.

[0050] Another aspect of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for outputting statistical results.

[0051] Based on the statistical result output method, apparatus, equipment, medium, and program products provided in this disclosure, by reading business data and structural tag trees, and associating the pre-built structural tag trees with tagged business data, automated management and classification statistics of business data within the statistical business scope are achieved. This enables automated, tagged classification management and summary statistics of different projects and different business results from different business perspectives. It solves the technical problems of high labor costs, low efficiency, and insufficient digitization in the statistical analysis of large amounts of business result data in related technologies. It achieves relatively efficient classification and summary of business results generated in the workflow, improves business result management efficiency, optimizes workflows, increases the work efficiency of business personnel, and reduces labor costs. Furthermore, by statistically analyzing business results within a specific business scope, it is easier to use statistical results to identify problems, promptly resolve issues in the business processing flow, and avoid risks. In addition, the structural tag tree establishes an inherent relationship between large amounts of business data, significantly reducing the computational resource consumption of computers performing association operations on large amounts of data while achieving automated tagged management of statistical data, thus improving the efficiency of computer data processing. Attached Figure Description

[0052] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0053] Figure 1 The illustration schematically depicts application scenarios of statistical result output methods, apparatuses, devices, media, and program products according to embodiments of the present disclosure;

[0054] Figure 2 A flowchart illustrating a method for outputting statistical results according to an embodiment of the present disclosure is shown schematically.

[0055] Figure 3 A schematic diagram illustrating a method for outputting statistical results according to an embodiment of the present disclosure is shown.

[0056] Figure 4 A schematic diagram illustrating the data structure of a structured tag tree according to an embodiment of the present disclosure is shown.

[0057] Figure 5 A schematic block diagram of a statistical result output device according to an embodiment of the present disclosure is shown; and

[0058] Figure 6 A block diagram schematically illustrates an electronic device suitable for implementing a statistical result output method according to an embodiment of the present disclosure. Detailed Implementation

[0059] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0060] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0061] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0062] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0063] In the embodiments disclosed herein, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of data (e.g., including but not limited to user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0064] In their work, business personnel need to categorize and statistically analyze business results to better understand the overall data. However, due to the sheer volume and unstructured nature of this data, statisticians often expend considerable effort searching through years of accumulated data to find the information they need. Managers also struggle to analyze the quantity and status of all business results from a holistic perspective, leading to high labor costs, low efficiency, insufficient digitization, and increased consumption of computing resources during the processing of massive amounts of data.

[0065] In view of this, embodiments of this disclosure provide a method for outputting statistical results, including:

[0066] Read multiple tagged data sets from the database; read a first structured label tree associated with statistical business fields from the database, whereby the statistical business fields characterize the statistical business scope associated with performing statistical operations on the multiple tagged data sets; based on the statistical business fields, associate the first structured label tree with the multiple tagged data sets to identify multiple target data sets from the multiple tagged data sets; perform statistical analysis on the multiple target data sets to output statistical results.

[0067] Figure 1 The illustration schematically depicts application scenarios of statistical result output methods, apparatus, devices, media, and program products according to embodiments of the present disclosure.

[0068] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0069] Users can interact with server 105 via network 104 using at least one of the first terminal device 101, second terminal device 102, and third terminal device 103 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, second terminal device 102, and third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0070] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0071] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0072] In the application scenarios of this disclosure embodiment, business personnel can interact with server 105 through first terminal device 101, second terminal device 102, and third terminal device 103 to initiate a request to the server for obtaining statistical results. In response to the user request, server 105 can be used to execute the statistical result output method of this disclosure embodiment. For example, it can read the tagged data and the corresponding structural label tree from the database, associate the tagged data and the structural label tree to generate statistical results, and return the statistical results to the business personnel through first terminal device 101, second terminal device 102, and third terminal device 103.

[0073] It should be noted that the statistical result output method provided in this embodiment can generally be executed by server 105. Correspondingly, the statistical result output device provided in this embodiment can generally be located in server 105. The statistical result output method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the statistical result output device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0074] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0075] The following will be based on Figure 1 The described scene, through Figures 2-6 The statistical result output method of the disclosed embodiments is described in detail.

[0076] Figure 2 A flowchart illustrating a method for outputting statistical results according to an embodiment of the present disclosure is shown schematically. Figure 3 A system diagram illustrating a statistical result output method according to an embodiment of the present disclosure is shown schematically. The following is in conjunction with... Figure 2 , Figure 3 The method for outputting statistical results according to embodiments of the present disclosure is described.

[0077] like Figure 2 As shown, the statistical result output method of this embodiment includes operations S201 to S204.

[0078] In operation S201, multiple sets of labeled data are read from the database;

[0079] In operation S202, the first structured label tree associated with the statistical business field is read from the database. The statistical business field is used to characterize the statistical business scope associated with performing statistical operations on multiple labeled data.

[0080] In operation S203, based on the statistical business field, the first structure label tree and multiple labeled data are associated so that multiple target data can be determined from the multiple labeled data.

[0081] In operation S204, statistical analysis is performed on multiple target data to output statistical results.

[0082] like Figure 3 As shown, before executing the statistical result output method of this embodiment, multiple structural tag trees are pre-built, maintained, and stored in a database. Each structural tag tree is associated with a type of business segment, and a structural tag tree can be constructed based on the business hierarchy relationship under the same business segment. The structural tag tree is a multi-level data structure, including multi-level node tags. The multi-level node tags of a structural tag tree can represent the business hierarchy logical relationship under the same business segment, wherein the root node corresponds to the business segment associated with the structural tag tree.

[0083] According to embodiments of this disclosure, the data being statistically analyzed can be various business outcome data generated from processing business results. Specifically, in auditing, the statistically analyzed data may include audit reports, audit issue data, evidence collection forms, audit projects, audit plans, audit working papers, etc. Before executing the statistical result output method of this embodiment, the statistically analyzed data is pre-labeled and stored in a database. Each labeled data point is pre-labeled with an identification path tag. The identification path tags can be generated in advance based on a pre-constructed structured tag tree to label the data. The identification path tag can be used to identify the business level associated with the data. For example, if an audit report is labeled "A (Small Business Credit) - B1 (Pre-Loan) - C1 (Pre-Loan Investigation)," it indicates that the audit business hierarchy and scope associated with this audit report are: the pre-loan investigation stage, which is the next level below the pre-loan stage in the small business credit business segment.

[0084] According to the embodiments of this disclosure, the statistical result output method of the embodiments of this disclosure first reads multiple tagged data from the database and reads the first structured tag tree associated with the statistical business fields through the above-described operations S201 and S202.

[0085] The statistical business field is used to characterize the scope of statistical business associated with performing statistical operations on multiple tagged data sets. The statistical business field can include multi-level statistical fields, which characterize multiple business levels involved in the statistical business scope. For example, the statistical business field "Small Business Credit - Pre-loan - Business Acceptance" can indicate that statistical analysis is required on data within the business acceptance stage of the pre-loan process under the small business credit business segment.

[0086] Retrieving the first structural label tree associated with a statistical business field from the database can be done by retrieving a structural label tree whose root node contains the header field of the statistical business field. This means retrieving the structural label tree associated with the corresponding business segment. For example, based on the header field "Small Business Credit" in the statistical business field "Small Business Credit," the structural label tree whose root node contains "Small Business Credit" is retrieved from the database as the first structural label tree associated with the statistical business field. This structural label tree is then associated with the "Small Business Credit" business segment.

[0087] According to embodiments of this disclosure, such as Figure 2 , Figure 3 As shown, after reading multiple tagged data from the database and reading the first structured label tree associated with the statistical business field, in the above operations S203 and S204, the first structured label tree and multiple tagged data are associated based on the statistical business field to output the statistical results.

[0088] Specifically, associating the first-structured label tree with multiple tagged data sets can involve identifying multiple target data sets (target data representing data generated within the statistical business scope) from the tagged data sets based on the association results. Furthermore, the quantity of these target data sets can be statistically analyzed to obtain statistical results, which can be used to characterize how many data sets were generated within that statistical business scope. These statistical results can also be used to identify problems. For example, if a large number of problematic data sets are generated within a certain statistical business scope, it indicates that there are many problems in the business processes within that scope, requiring close attention to those processes.

[0089] According to embodiments of this disclosure, a structured label tree can represent the hierarchical logical relationship of business within the same business segment. Data is pre-labeled with identification path tags, which can be pre-generated based on the constructed structured label tree and used to identify the hierarchical relationship of business within a specific business scope associated with the data. Therefore, based on the statistical business scope represented by the statistical business field, the first structured label tree and multiple labeled data can be associated using structured identification path tags to obtain the number of data generated within that statistical business scope.

[0090] According to embodiments of this disclosure, by reading business data and a structured tag tree, and associating the pre-built structured tag tree with the tagged business data, automated management and classification statistics of business data within the scope of statistical business are achieved. This enables automated, tagged classification management and summary statistics of different projects and different business results from different business perspectives. It solves the technical problems of high manpower costs, low efficiency, and insufficient digitization in the statistical analysis of large amounts of business result data in related technologies. It achieves more efficient classification and summary of business results generated in the workflow, improves business result management efficiency, optimizes workflows, increases the work efficiency of business personnel, and reduces manpower costs. Furthermore, by statistically analyzing business results within a specific business scope, it is easier to use the statistical results to identify problems, promptly resolve issues in the business processing flow, and avoid risks. In addition, the structured tag tree establishes an inherent relationship between large amounts of business data. Based on the automated management of tagged statistical data, it significantly reduces the computational resource consumption of computers performing association operations on large amounts of data, improving the efficiency of computer data processing.

[0091] According to embodiments of this disclosure, before executing the statistical result output method of this embodiment, multiple structural tag trees are pre-built, maintained, and stored in a database. Each structural tag tree is associated with a different business segment, and each structural tag tree is associated with a type of business segment. Business segments are divided according to their business scope, such as including comprehensive auditing, financial technology, economic responsibility, credit business for large and medium-sized enterprises, personal loan business, financial accounting management, credit card business, etc., enabling integrated management based on different business segments.

[0092] Each structural tag tree includes multi-level node tags, which in turn include a root node tag and multi-level child node tags. The root node tag corresponds to the business segment associated with the structural tag tree, and the multi-level child node tags correspond to multiple business levels within the business segment associated with the structural tag tree. A structural tag tree can be constructed based on the business hierarchy relationships within the same business segment.

[0093] Figure 4 A schematic diagram illustrating the data structure of a structured tag tree according to an embodiment of the present disclosure is shown.

[0094] like Figure 4As shown, the structured label tree adopts a tree-like data structure. Taking the "Small Business Lending" business segment as an example, the root node label of the structured label tree associated with this segment is "A (Small Business Lending)". Second-level child node labels may include "B1 (Pre-loan)", "B2 (During Loan)", and "B3 (Post-loan)", representing the next-level business classification relationship within the Small Business Lending segment. Specifically, the next-level child node labels associated with the second-level child node label "B1 (Pre-loan)" may include "C1 (Credit Access)" and "C2 (Business Acceptance)", representing the next-level business classification relationship within the pre-loan stage of the Small Business Lending segment.

[0095] According to embodiments of this disclosure, since a single business segment can involve numerous sub-business areas, the hierarchical classification and grading relationships within the same business segment are also quite complex, exhibiting a nested hierarchical business logic relationship. By establishing a tree-like data structure tag tree, the hierarchical classification and grading relationships within the same business segment can be presented intuitively and systematically, facilitating user understanding and reducing the user's comprehension cost. Simultaneously, for personnel maintaining the structure tags, the structural logic is relatively simple, making it easy to maintain and understand. By ensuring consistency between the segment name and the root node tag name, the business scope can be better identified, enabling more business-specific statistics on business results.

[0096] According to embodiments of this disclosure, the structured tag tree can be applied to project initiation and scheme preparation, and can serve as an important basis for the classification, management, and summary statistics of business activities and business results. After the structured tag tree is constructed, it needs to be maintained and updated in a timely manner as the scope of business changes. Therefore, the above method also includes maintenance operations on the node tags in the structured tag tree.

[0097] Business personnel can view the structured tag tree of all sections. Each section can have multiple section administrators, who have the authority to maintain their respective sections.

[0098] Maintenance operations on node labels in the structure label tree may include at least one of the following: adding node labels, modifying node label names, modifying the display order of node labels, moving node labels, merging node labels, disabling node labels, and enabling node labels.

[0099] Specifically, adding a new node label can be done by adding a child label node under a parent label node. For example, in Figure 4 In the structured label tree shown, under node A, new nodes of type B, B4, B5, B6, etc., are added.

[0100] Specifically, modifying a node label name can involve changing the name of any node label, for example, changing the name of the node label. Figure 4 The name of node B1 shown is changed to B0.

[0101] Specifically, in the structural tag tree, multiple sibling child nodes under the same parent node are set to display in a specific order, for example... Figure 4 In the structured tag tree shown, the display order of the B-type nodes under node A is B1, B2, B3. Modifying the display order of node tags refers to changing the arrangement order of a structural tag among its sibling nodes without changing the parent node's tag. For example, it could be modifying the display order of multiple sibling child nodes under the same parent node, such as changing the display order of the B-type nodes under node A to B2, B1, B3.

[0102] Specifically, moving a node label can be done by moving a node label and all its child nodes to other nodes, that is, changing the parent structure label to which the node label belongs. After a node label is moved, it will be the last child node label of the new parent label by default.

[0103] Specifically, merging node labels can be done by merging node labels under the same parent node. The first selected node label is merged into the second selected node label. If the merged node label has child node labels, all child node labels of the merged node label are automatically adopted into the merged node label.

[0104] Specifically, deactivating a node label can be done by deactivating a specific node label according to business needs. After deactivating the node label, the node label and all its child node labels will be deactivated from top to bottom.

[0105] Specifically, enabling a node label can be done by enabling a certain node label according to business needs. After enabling the node label, the node label and all its disabled parent node labels will be enabled from bottom to top.

[0106] According to the embodiments disclosed in this announcement, during the maintenance of node tags in the structure tag tree, when a section administrator maintains the structure tag of a section, before the maintenance operation is submitted and becomes effective, the maintenance operation can be revoked and the section can be restored to the current version of the structure tag.

[0107] According to the implementation method described in this announcement, during the maintenance of node tags in the structure tag tree, each maintenance of structure tags by the module administrator is temporary and does not take effect immediately. In order to avoid accidental operation, all maintenance operations are allowed to take effect only after clicking "Submit to Take Effect". Only after the structure tags take effect can they be viewed and used by business personnel.

[0108] According to the embodiments described in this announcement, during the maintenance of node tags in the structural tag tree, there may be situations where multiple people are involved in the maintenance. Each section allows multiple section administrators, and structural tags can be maintained simultaneously by multiple administrators. When multiple people are maintaining the tags, the version number of the structural tag determines whether the maintenance operation is allowed to take effect. For example, if the effective version number of the structural tag for the personal loan business section is V1, and three people (A, B, and C) simultaneously perform maintenance operations without submitting them for effect, the version number of the structural tag maintenance status for the personal loan business section will all be V1 for A, B, and C. However, if A subsequently submits the structural tag for the personal loan business section for effect, the effective version number of the structural tag for that section will be automatically incremented by 1, updating to V2. If B and C subsequently attempt to submit for effect, the system will prompt that the current maintenance version number is inconsistent with the effective version number (V1 and V2 are inconsistent), requiring B and C to cancel the current maintenance and perform it again.

[0109] According to the embodiments described in this announcement, during the maintenance of node tags in the structural tag tree, operation logs can also be maintained. To facilitate better maintenance of structural tags for users, maintenance logs for structural tags are provided. There are two types of structural tag maintenance logs: one is a historical maintenance operation log of all submissions that have taken effect for this structural tag, and the other is a maintenance log recorded by the forum administrator during the maintenance of a specific structural tag (before it is officially submitted and takes effect). The main record fields of the log include "Operation Type (Add, Modify, Move Up, Move Down, Move, Enable, Disable, Merge), Current Tag, Current Parent Tag, Original Tag, Original Parent Tag, Operator, Operation Time," used to record operation traces.

[0110] According to the embodiments of this disclosure, in order to improve maintenance efficiency and user experience for maintenance personnel, under the premise of maintenance operations such as adding, modifying, moving, enabling, and disabling, multi-person maintenance, undo maintenance, and maintenance log are designed to improve multi-person management capabilities, fault tolerance capabilities, and log recording capabilities, ensuring that every operation is traceable and allowing undoing of erroneous operations.

[0111] According to embodiments of this disclosure, during the construction and maintenance of the structured label tree, the node labels must satisfy certain combination rules.

[0112] For example, the combination rules that node labels must satisfy include: in a multi-level node label structure tree, multiple node labels of the same level that are associated with the same parent node label must have different names; that is, sibling nodes are not allowed to have the same name, while cousin nodes are allowed to have the same name. Among these, the parent node of sibling nodes must be the same. For example... Figure 4 In the structural tag tree, nodes C1 and C2 are sibling nodes. Cousins ​​have different parent nodes, but their parent node is a sibling node. For example... Figure 4 In the structured tag tree, nodes C2 and C3 are sibling nodes.

[0113] In the structural tag tree, for each node tag, there exists a unique full tag path. The full tag path refers to the combination of the node tag itself and all (multiple) parent node tags associated with that node tag. For example... Figure 4 The full path of the label for node C1 in the structured label tree is "A-B1-C1".

[0114] By ensuring that the names of multiple nodes at the same level that are associated with the same parent node are different, we can guarantee that the full paths of all structural tags are not repeated.

[0115] In the multi-level node tags of the structural tag tree, the status of each node tag is enabled or disabled.

[0116] For example, the combination rules that node labels must satisfy also include: the multi-level lower-level node labels of a disabled node label must be disabled, and the multi-level upper-level node labels of an enabled node label must be enabled. That is, on the entire label path of a node label, there must be no situation where the enabled and disabled states of a node label are interleaved (at least 3 consecutive nodes), there cannot be an enabled node between two disabled nodes, and there cannot be a disabled node between two enabled nodes.

[0117] By limiting this rule, it can be ensured that all child tags of a structure tag in a disabled state are disabled, and that all parent tags of a structure tag in an enabled state are enabled.

[0118] According to embodiments of this disclosure, by setting combination rules that must be satisfied between the above-mentioned node labels, it can be ensured that the business hierarchy relationship reflected in the structure tree does not become disordered.

[0119] According to embodiments of this disclosure, before executing the statistical result output method of this embodiment, the data is pre-labeled and stored in a database. Labeling the data involves associating a structured tag tree with the data, determining which specific node tag in the structured tag tree the data to be labeled should be associated with based on the business scope involved, and further generating an identification path tag for the data to be labeled based on that node tag and the structured tag tree. When creating a business outcome, one or more sets of identification path tags can be selected to establish an association with the business outcome.

[0120] Specifically, the methods for tagging business data include the following operations.

[0121] Operation 1: Read the second structure tag tree associated with the tagging field from the database, specifically the structure tag tree corresponding to the business segment to which the data to be tagged belongs.

[0122] The tagging field is used to characterize the scope of the tagging business associated with performing the tagging operation on the data to be tagged, i.e., the business scope to which the data to be tagged belongs. Tagging fields can include multi-level tagging fields, which are used to characterize multiple business levels within the business scope involved in the data to be tagged. For example, the tagging field "Small Business Credit - Pre-loan - Business Acceptance" indicates that the business scope (hierarchical relationship) to which the data to be tagged belongs is the business acceptance stage within the pre-loan stage of the small business credit business segment.

[0123] Retrieving a second structured tag tree associated with a labeled field from the database can be achieved by retrieving a structured tag tree whose root node contains the header field of the labeled field. For example, based on the header field "Small Business Credit - Pre-Loan - Business Acceptance" in the labeled field "Small Business Credit", the database can be used to retrieve a structured tag tree whose root node contains "Small Business Credit" as the second structured tag tree, which is then associated with the "Small Business Credit" business segment.

[0124] Operation 2: Generate identification path labels based on the tagging fields and the second structure tag tree.

[0125] Specific methods may include: First, based on the tagging field, determining the second target node label corresponding to the tagging business scope from the second-structure tag tree. That is, based on the business scope involved in the tagging field, determining which specific node label in the second-structure tag tree the data to be tagged should be associated with. This could be based on the tagging field, determining the second target node label corresponding to the last-level tagging field of the tagging field from the second-structure tag tree. For example, using... Figure 4 Taking the label tree structure shown as an example, based on the labeling field "Small Business Credit - Pre-loan - Business Acceptance", the nodes "B1 (Pre-loan)" - "C2 (Business Acceptance)" are queried sequentially from the upper level to the lower level based on the root node "A (Small Business Credit)" of the second structure label tree. The second target node label is determined to be node C2, which corresponds to the last-level labeling field "Business Acceptance".

[0126] Then, using the second target node label as the path terminator, the multi-level parent node labels associated with the second target node label in the second structure label tree are used as the parent path nodes of the path terminator to generate the identifying path label. For example, using... Figure 4 Taking the label tree structure shown as an example, with node C2 as the end node of the path, the full path label of the node is generated by combining the labels of the multi-level parent nodes of this node: "A-B1-C2", which serves as the identifier path label.

[0127] According to embodiments of this disclosure, the tags for business data are displayed as full-path tags (the full path consisting of the target node and its multiple parent nodes). As described in the example above, multiple data tags are displayed as "A-B1-C2" to show the full path, rather than just displaying it as tag C2. This provides more complete information and facilitates a comprehensive understanding of the parent business domains and scope associated with the business data. Using structural tags for the full path also enables better aggregation and statistics, allowing for the generation of relevant statistical reports.

[0128] Operation 3: After marking the data to be marked using the identification path labels, the marked data is generated.

[0129] According to embodiments of this disclosure, by tagging the data, it can be subsequently associated with a tag tree, which facilitates the tag-based management of business results, enabling better classification, management, and aggregation of statistical data.

[0130] According to embodiments of this disclosure, the following rules must be followed when labeling data (establishing associations):

[0131] Parent and child path labels are not allowed to be associated with the same business outcome simultaneously. For example, when labeling a report, if it is labeled "A-B1-C1", and then "A-B1-C1-D1" is selected, "A-B1-C1-D1" will replace "A-B1-C1" to ensure that parent and child path labels do not coexist. However, sibling path labels are allowed to coexist; that is, "A-B1-C1" and "A-B1-C2" can be associated with the same business outcome. If this business outcome is subsequently labeled "A-B1", then "A-B1" will replace both "A-B1-C1" and "A-B1-C2".

[0132] According to embodiments of this disclosure, after modifying the name of a node label in the structure label tree, the label of the tagged data needs to be modified simultaneously, and the full path of the structure label needs to be updated synchronously.

[0133] Business outcomes are associated with structural tags. After a business outcome is successfully implemented, as the business progresses and changes, the structural tags associated with the implemented business outcome can be adjusted so that the relevant business outcome data can be recalculated based on the structural tags.

[0134] According to embodiments of this disclosure, the statistical business field includes multi-level statistical fields, which are used to characterize multiple business levels involved in the statistical business scope. Based on the first structured label tree already obtained and associated with the statistical business field, and the tagged data, the first structured label tree and multiple tagged data are associated to determine how many data sets were generated within the statistical business scope, thus obtaining the statistical results.

[0135] Specifically, further operations that associate the first structured label tree with multiple tagged data based on statistical business fields may include:

[0136] First, multiple statistical path labels are generated based on the statistical business fields and the first structure label tree; then, the labeled data with the same identifier path label as the statistical path label are identified as target data, and the statistical results are obtained by counting the number of target data.

[0137] According to embodiments of this disclosure, data is pre-labeled with identification path tags. These identification path tags are generated based on a constructed structural tag tree and are used to identify the business hierarchy within a specific business scope associated with the data. The structural tag tree can represent the business hierarchy within the same business segment. Therefore, based on the statistical business scope represented by the statistical business field, the node tag corresponding to that business scope can be determined from the first structural tag tree. Furthermore, based on the node tag corresponding to that business scope, a full path tag for that node tag is generated as a statistical path tag. Then, all report data labeled with this tag is statistically analyzed as the statistical result. By generating multiple statistical path tags using the first structural tag tree, tagged statistics of data can be achieved, facilitating effective classification, management, and summary statistics of business results.

[0138] For example, based on the statistical business field "Small Business Lending - Pre-loan - Business Acceptance", it is known that statistics need to be compiled on the business scope involved in the business acceptance stage within the pre-loan stage of the small business lending business segment. Further, based on the statistical business scope represented by the statistical business field, the node label corresponding to that business scope can be determined from the first structured label tree, for example, using... Figure 4 Taking the label tree structure shown as an example, you can query the nodes "B2 (pre-loan)" to "C3 (business acceptance)" sequentially from the root node "A (small business credit)" to the lower level to determine the node label corresponding to the business scope as "C3". Then, based on the node label corresponding to the business scope, generate the full path label "A-B2-C3" of the node label as the statistical path label. After that, count all report data with this label as the statistical result.

[0139] Furthermore, generating multiple statistical path labels based on statistical business fields and the first structured label tree can include the following operations:

[0140] First, based on the lowest-level statistical field in the statistical business fields, determine the first target node label corresponding to the lowest-level statistical field from the first structural label tree. For example, using... Figure 4 Taking the label tree structure shown as an example, based on the statistical business field "Small Business Credit - Pre-loan - Business Acceptance" (which indicates that the business data involved in the business acceptance stage of the pre-loan stage under the small business credit business segment is required to be statistically analyzed), the first target node label corresponding to the last-level statistical field is determined from the first structure label tree as follows: Based on the root node "A (Small Business Credit)" of the structure label tree, the nodes "B2 (Pre-loan)" - "C3 (Business Acceptance)" are queried sequentially from the upper level to the lower level, and the first target node label is determined to be node C3. This node corresponds to the last-level statistical field "Business Acceptance" of the labeling field.

[0141] Then, using the first target node label as the path boundary, the multi-level parent node labels associated with the first target node label in the first structural label tree are taken as the parent path nodes of the path boundary, and the multi-level child node labels associated with the first target node label in the first structural label tree are taken as the child path nodes of the path boundary, thus generating multiple statistical path labels. That is, once the path boundary label is determined, the full path labels corresponding to each level of sub-labels under that path boundary label are taken as statistical path labels.

[0142] For example, with Figure 4 Taking the label tree structure shown as an example, with node C3 as the path boundary, multiple statistical path labels are generated by combining the labels of its multiple parent and child nodes: "A-B2-C3", "A-B2-C3-D2", "A-B2-C3-D3", and "A-B2-C3-D4". During the statistical analysis, all report data with the above labels will be counted.

[0143] For example, with Figure 4 Taking the label tree structure shown as an example, with node B1 as the path boundary, multiple statistical path labels are generated by combining the labels of its multiple-level parent nodes and multiple-level child nodes: "A-B1", "A-B1-C1", "A-B1-C2", and "A-B1-C1-D1". During the statistical analysis, all report data with the above labels will be counted.

[0144] According to embodiments of this disclosure, by determining the first target node label corresponding to the last-level statistical field in the statistical business field from the first structural label tree, the node label corresponding to the statistical business scope in the structural label tree is determined. Since the structural label tree is constructed based on business hierarchy, the business scope represented by all child nodes under the first target node label is within the business scope covered by the first target node label. Furthermore, the business scope associated with multiple statistical path labels generated based on all lower-level node labels of the first target node label is within the business scope covered by the first target node label. By counting the number of business results affixed with the aforementioned statistical path labels, all business result data involved within the statistical business scope can be obtained. Therefore, based on automatic statistics achieved through label management, the business result statistics obtained by this method are more accurate and comprehensive, without omitting statistical data, and have strong reference value, providing accurate data support for the discovery of subsequent problems.

[0145] Based on the above-described statistical result output method, this disclosure also provides a statistical result output device. The following will be combined with... Figure 5 The device is described in detail.

[0146] Figure 5 A schematic block diagram of a statistical result output device according to an embodiment of the present disclosure is shown.

[0147] like Figure 5 As shown, the statistical result output device 500 of this embodiment includes a first reading module 501, a second reading module 502, an association module 503, and a statistical module 504.

[0148] The first reading module 501 is used to read multiple sets of tagged data from the database;

[0149] The second reading module 502 is used to read the first structured label tree associated with the statistical business field from the database, wherein the statistical business field is used to characterize the statistical business scope associated with performing statistical operations on multiple labeled data.

[0150] The association module 503 is used to associate the first structure label tree with multiple labeled data based on statistical business fields, so as to determine multiple target data from the multiple labeled data.

[0151] The statistics module 504 is used to perform statistical analysis on multiple target data to output statistical results.

[0152] According to embodiments of this disclosure, business data and a structural tag tree are read by a first reading module 501 and a second reading module 502, and the pre-built structural tag tree and the tagged business data are associated by an association module 503. This achieves automated management and classification statistics of business data within the scope of statistical business, enabling automated and tagged classification management and summary statistics of different projects and business results from different business perspectives. This solves the technical problems of high labor costs, low efficiency, and insufficient digitization in the statistical analysis of large amounts of business result data in related technologies. It achieves more efficient classification and summary of business results generated in the workflow, improves business result management efficiency, optimizes workflows, increases the work efficiency of business personnel, and reduces labor costs. Furthermore, by statistically analyzing results within a specific business scope, it is easier to use the statistical results to identify problems, promptly resolve issues in the business processing flow, and avoid risks.

[0153] According to an embodiment of this disclosure, each tagged data is marked with an identification path label, and the association module 503 includes a first generation unit and a first determination unit.

[0154] The first generation unit is used to generate multiple statistical path labels based on statistical business fields and a first structure label tree; the first determination unit is used to determine the labeled data with the same identifier path label as the statistical path label as the target data.

[0155] According to embodiments of this disclosure, the statistical business field includes multi-level statistical fields, which are used to characterize multiple business levels involved in the statistical business scope. The first structural label tree includes multi-level node labels, which correspond to multiple business levels associated with the first structural label tree.

[0156] The first generation unit includes a determination subunit and a generation subunit.

[0157] The determination subunit is used to determine the first target node label corresponding to the last-level statistical field in the statistical business field from the first structure label tree; the generation subunit is used to take the first target node label as the path boundary point, take the multi-level parent node labels associated with the first target node label in the first structure label tree as the parent path node of the path boundary point, and take the multi-level child node labels associated with the first target node label in the first structure label tree as the child path node of the path boundary point, so as to generate multiple statistical path labels.

[0158] According to embodiments of this disclosure, the above-described apparatus further includes a third reading module, a generation module, and a marking module.

[0159] The third reading module is used to read the second structure tag tree associated with the tagging field from the database. The tagging field is used to represent the tagging business scope associated with the tagging operation performed on the data to be tagged. The generation module is used to generate identification path tags based on the tagging field and the second structure tag tree. The tagging module is used to generate tagged data after tagging the data to be tagged using the identification path tags.

[0160] According to embodiments of this disclosure, the generation module includes a second determining unit and a second generation unit.

[0161] The second determining unit is used to determine the second target node label corresponding to the labeling business scope from the second structure label tree based on the labeling field; the second generating unit is used to generate an identifier path label by taking the second target node label as the path end node and taking the multi-level parent node labels associated with the second target node label in the second structure label tree as the parent path nodes of the path end node.

[0162] According to embodiments of this disclosure, the database stores multiple structural tag trees, each of which is associated with a different business segment. Each structural tag tree includes multi-level node tags, which include root node tags and multi-level child node tags. The root node tags correspond to the business segments associated with the structural tag tree, and the multi-level child node tags correspond to multiple business levels under the business segments associated with the structural tag tree.

[0163] According to embodiments of this disclosure, the above-described apparatus further includes a maintenance module for performing maintenance operations on node labels in a structural label tree, wherein the maintenance operations include at least one of the following: adding node labels, modifying node label names, modifying the display order of node labels, moving node labels, merging node labels, disabling node labels, and enabling node labels.

[0164] According to embodiments of this disclosure, in the multi-level node tags in the structural tag tree, the names of multiple same-level node tags associated with the same parent node tag are different; in the multi-level node tags in the structural tag tree, the state of each node tag is enabled or disabled, wherein the state of the multi-level lower-level node tags of a disabled node tag is disabled, and the state of the multi-level parent node tags of an enabled node tag is enabled.

[0165] According to embodiments of this disclosure, any multiple modules among the first reading module 501, the second reading module 502, the association module 503, and the statistics module 504 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the first reading module 501, the second reading module 502, the association module 503, and the statistics module 504 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in a suitable combination of any of these. Alternatively, at least one of the first reading module 501, the second reading module 502, the association module 503, and the statistics module 504 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0166] Figure 6 A block diagram schematically illustrates an electronic device suitable for implementing a statistical result output method according to an embodiment of the present disclosure.

[0167] like Figure 6 As shown, an electronic device 600 according to an embodiment of this disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage portion 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this disclosure.

[0168] RAM 603 stores various programs and data required for the operation of electronic device 600. Processor 601, ROM 602, and RAM 603 are interconnected via bus 604. Processor 601 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 602 and / or RAM 603. It should be noted that the programs may also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0169] According to embodiments of this disclosure, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to a bus 604. The electronic device 600 may also include one or more of the following components connected to the I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 606 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 610 as needed so that computer programs read from it can be installed into the storage section 608 as needed.

[0170] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0171] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 602 and / or RAM 603 and / or one or more memories other than ROM 602 and RAM 603 described above.

[0172] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the statistical result output method provided in the embodiments of this disclosure.

[0173] When the computer program is executed by the processor 601, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0174] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and / or installed from the removable medium 611. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0175] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0176] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0177] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0178] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0179] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A statistical result output method, comprising: reading a plurality of marked data from a database; reading a first structure label tree associated with a statistical business field from the database, wherein the statistical business field is used to represent a statistical business scope associated with a statistical operation performed on the plurality of marked data; reading a second structure label tree associated with a marking field from the database, wherein the marking field is used to represent a marking business scope associated with a marking operation performed on to-be-marked data; generating an identification path label based on the marking field and the second structure label tree; generating marked data by marking the to-be-marked data using the identification path label; associating the first structure label tree and the plurality of marked data based on the statistical business field, so that determining a plurality of target data from the plurality of marked data comprises: generating a plurality of statistical path labels based on the statistical business field and the first structure label tree; and determining marked data with an identification path label same as the statistical path label as the target data; performing statistical analysis on the plurality of target data to output a statistical result.

2. The method of claim 1, wherein, The statistical business field comprises a plurality of statistical fields, and the plurality of statistical fields are used to represent a plurality of business levels involved in the statistical business scope, and the first structure label tree comprises a plurality of node labels corresponding to the plurality of business levels associated with the first structure label tree. Generating a plurality of statistical path labels based on the statistical business field and the first structure label tree comprises: determining a first target node label corresponding to a last statistical field in the statistical business field from the first structure label tree; taking the first target node label as a path boundary point, taking a plurality of upper node labels associated with the first target node label in the first structure label tree as upper path nodes of the path boundary point, and taking a plurality of lower node labels associated with the first target node label in the first structure label tree as lower path nodes of the path boundary point, to generate a plurality of statistical path labels.

3. The method of claim 1, wherein, Generating an identification path label based on the marking field and the second structure label tree comprises: determining a second target node label corresponding to the marking business scope from the second structure label tree based on the marking field; taking the second target node label as a path end node, and taking a plurality of upper node labels associated with the second target node label in the second structure label tree as upper path nodes of the path end node, to generate the identification path label. 4.The method according to any one of claims 1-3, wherein: a plurality of structure label trees are stored in the database, and the plurality of structure label trees are respectively associated with different business blocks, each of the structure label trees comprises a plurality of node labels, the plurality of node labels comprise a root node label and a plurality of child node labels, the root node label corresponds to a business block associated with the structure label tree, and the plurality of child node labels correspond to a plurality of business levels under the business block associated with the structure label tree.

5. The method of claim 4, further comprising: maintaining the node tags in the structure tag tree, wherein the maintaining comprises at least one of adding a node tag, modifying a node tag name, modifying a node tag display order, moving a node tag, merging node tags, disabling a node tag, and enabling a node tag.

6. The method of claim 4, wherein: in the multi-level node tags in the structure tag tree, names of multiple same-level node tags associated to a same upper-level node tag are different; in the multi-level node tags in the structure tag tree, a state of each node tag is either enabled or disabled, wherein multi-level lower-level node tags of a disabled state node tag are disabled, and multi-level upper-level node tags of an enabled state node tag are enabled.

7. A statistical result output apparatus, comprising: a first reading module configured to read multiple pieces of tagged data from a database; a second reading module configured to read a first structure tag tree associated to a statistical service field from the database, wherein the statistical service field is used to represent a statistical service range associated to a statistical operation performed on the multiple pieces of tagged data; a third reading module configured to read a second structure tag tree associated to a tagging field from the database, wherein the tagging field is used to represent a tagging service range associated to a tagging operation performed on to-be-tagged data; a generating module configured to generate an identification path tag based on the tagging field and the second structure tag tree; a tagging module configured to tag the to-be-tagged data with the identification path tag to generate the tagged data; an associating module configured to associate the first structure tag tree and the multiple pieces of tagged data based on the statistical service field, so as to determine multiple pieces of target data from the multiple pieces of tagged data, wherein the associating module comprises a first generating unit and a first determining unit, the first generating unit is configured to generate multiple statistical path tags based on the statistical service field and the first structure tag tree, and the first determining unit is configured to determine the tagged data with the same identification path tag and statistical path tag as the target data; a statistical module configured to perform statistical analysis on the multiple pieces of target data to output a statistical result.

8. An electronic device, comprising: one or more processors; a storage device configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method according to any one of claims 1-6.

9. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method according to any one of claims 1-6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

Images