A data processing method and related device for an organizational structure

Abstract

The present disclosure relates to a data processing method and related device for an organizational structure, the method comprising: obtaining detailed data associated with a most fine-grained node of an organizational structure; and prepositioning the detailed data into a respective dedicated data storage area of each ancestor node of the most fine-grained node according to a pre-established and maintained organizational structure hierarchy bridging table, so that the dedicated data storage area of any ancestor node includes all detailed data belonging to the ancestor node on the organizational structure; and the organizational structure hierarchy bridging table includes a path relationship from each most fine-grained node in the organizational structure to at least one ancestor node of the most fine-grained node. The present disclosure provides a data localization basis for subsequent queries, so that the time complexity of the subsequent query response is reduced from the original aggregation calculation complexity which increases with the square of the number of nodes to a nearly linear logarithmic level, thereby achieving a quantitative improvement in response efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and more specifically, to a data processing method and related apparatus oriented towards organizational structure. Background Technology

[0002] Currently, the organizational structures of large enterprises are typically unbalanced tree structures (such as nested structures like group-industry-region-company), which are quite complex. When management within this structure needs to perform detailed data (such as salary data) aggregation, analysis, and monitoring at any subordinate level, the usual approach is to dynamically filter data belonging to the target node from the full set of detailed data through complex recursive join operations during the query process, and then perform calculations. However, due to the massive volume of detailed data and the complexity of organizational relationships within enterprises, this process is extremely time-consuming and cannot meet the requirements for real-time response. Summary of the Invention

[0003] The purpose of this disclosure is to provide a data processing method and related apparatus for organizational structures, which can provide data conditions to shorten the response time of penetrating processing in order to meet real-time response requirements.

[0004] To achieve the above objectives, in a first aspect, this disclosure provides a data processing method oriented towards organizational structure, which may include: Retrieve detailed data associated with the finest-grained nodes of the organizational structure; Based on the pre-established and maintained organizational structure hierarchical bridging table, detailed data is pre-set into the dedicated data storage area corresponding to each ancestor node of the finest granularity node, so that the dedicated data storage area corresponding to any ancestor node includes all detailed data belonging to the ancestor node in the organizational structure; the organizational structure hierarchical bridging table includes: the path relationship from each finest granularity node in the organizational structure to at least one ancestor node of the finest granularity node.

[0005] In one possible implementation, detailed data is pre-set into a dedicated data storage area corresponding to each ancestor node of the finest-grained node, including: The detailed data is copied to the physical or logical storage area corresponding to each ancestor node of the finest-grained node.

[0006] In one possible implementation, the method further includes: In response to a query request for a target node, the system performs the corresponding processing operation on the detailed data in the dedicated data storage area corresponding to the target node and outputs the processing result; the target node can be any node in the organizational structure.

[0007] In one possible implementation, the detailed data associated with the finest-grained node of the organizational structure is encrypted; the processing operation corresponding to the query request is performed on the detailed data in the dedicated data storage area corresponding to the target node, including: Based on the homomorphic encryption algorithm, the processing operation corresponding to the query request is performed on the detailed data in the dedicated data storage area.

[0008] In one possible implementation, in response to a query request for a target node, the processing operation corresponding to the query request is performed on the detailed data in the dedicated data storage area corresponding to the target node, and the processing result is output, including: For any dedicated data storage area, perform pre-aggregation processing on the detailed data in the dedicated data storage area to obtain the pre-aggregation result; In response to a query request for a target node, the pre-aggregated result corresponding to the query request is used as the processing result.

[0009] In one possible implementation, the query request carries the time period to be queried, and the method further includes: Receive enterprise data; The organizational structure hierarchy bridging table is updated based on enterprise data. The organizational structure hierarchy bridging table carries a version identifier to indicate the effective time. In response to a query request for a target node, the system performs the corresponding processing operations on the detailed data in the dedicated data storage area corresponding to the target node, and outputs the processing results, including: In response to a query request for a target node, determine one or more versions of the organizational structure hierarchy bridging table that are in effect during the query period based on the version identifier; For each version of the organizational structure hierarchical bridging table, the target detailed data whose generation time is within the time period to be queried is determined from the dedicated data storage area corresponding to the target node; Perform the corresponding processing operation on the target detailed data and output the processing result.

[0010] Secondly, this disclosure provides a data processing apparatus oriented towards organizational structure, the apparatus comprising: The acquisition module is used to retrieve detailed data associated with the finest-grained nodes of the organizational structure; The pre-configured module is used to pre-configure detailed data into the dedicated data storage area corresponding to each ancestor node of the finest granularity node according to the pre-established and maintained organizational structure hierarchical bridging table, so that the dedicated data storage area corresponding to any ancestor node includes all detailed data belonging to the ancestor node in the organizational structure; the organizational structure hierarchical bridging table includes: the path relationship from each finest granularity node in the organizational structure to at least one ancestor node of the finest granularity node.

[0011] In one possible implementation, the preset module is specifically used for: The detailed data is copied to the physical or logical storage area corresponding to each ancestor node of the finest-grained node.

[0012] In one possible implementation, the device further includes: The query module is used to respond to query requests for a target node, perform the corresponding processing operations on the detailed data in the dedicated data storage area corresponding to the target node, and output the processing results; the target node can be any node in the organizational structure.

[0013] In one possible implementation, detailed data associated with the finest-grained nodes of the organizational structure is encrypted; the query module is specifically used for: Based on the homomorphic encryption algorithm, the processing operation corresponding to the query request is performed on the detailed data in the dedicated data storage area.

[0014] In one possible implementation, the query module is specifically used for: For any dedicated data storage area, perform pre-aggregation processing on the detailed data in the dedicated data storage area to obtain the pre-aggregation result; In response to a query request for a target node, the pre-aggregated result corresponding to the query request is used as the processing result.

[0015] In one possible implementation, the query request carries a time period to be queried, and the apparatus further includes: The receiving module is used to receive enterprise data; The update module is used to update the organizational structure hierarchy bridging table based on enterprise data. The organizational structure hierarchy bridging table carries a version identifier to indicate the effective time. The query module is specifically used for: In response to a query request for a target node, determine one or more versions of the organizational structure hierarchy bridging table that are in effect during the query period based on the version identifier; For each version of the organizational structure hierarchical bridging table, the target detailed data whose generation time is within the time period to be queried is determined from the dedicated data storage area corresponding to the target node; Perform the corresponding processing operation on the target detailed data and output the processing result.

[0016] Thirdly, this disclosure provides an electronic device, including: A memory on which computer programs are stored; A processor is used to execute computer programs in memory to implement the organizational-oriented data processing method described in the first aspect.

[0017] Fourthly, this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the organizational structure-oriented data processing method described in the first aspect.

[0018] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the organizational structure-oriented data processing method described in the first aspect.

[0019] Through the above technical solution, a hierarchical bridging table including organizational relationships is dynamically maintained. Based on the hierarchical bridging table, detailed data associated with the finest-grained node is pre-positioned into the storage areas corresponding to all its ancestor nodes, thus pre-constructing a complete local dataset for each organizational node. In this way, subsequent query requests for any node can be processed directly based on the fully aggregated data in the storage area corresponding to that node, without needing to perform recursive traversal of the organizational tree and dynamic filtering of the entire dataset during the query. Compared to the serial processing of organizational relationship resolution and global data scanning that must be dynamically performed at the query time in related technologies, this disclosure shifts the high-overhead processing stage of organizational relationship resolution and global data scanning to be completed concurrently at the data entry time, providing a data localization foundation for subsequent queries. This reduces the time complexity of subsequent query responses from the aggregation computation complexity that originally increased quadratically with the number of nodes to near linear logarithmic levels, thereby achieving an order-of-magnitude improvement in response efficiency.

[0020] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This disclosure provides a data processing method oriented towards organizational structure. Figure 2 A schematic diagram of a data processing system architecture oriented towards organizational structure is provided for embodiments of this disclosure; Figure 3 A schematic diagram of a data processing apparatus oriented towards organizational structure provided in an embodiment of this disclosure; Figure 4 This is a block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0022] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0023] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0024] Currently, when management in an enterprise's organizational structure needs to perform detailed data (such as salary data) aggregation, analysis, and monitoring at any subordinate level, the process typically involves dynamically filtering data belonging to the target node from the full set of detailed data based on fixed organizational relationships through complex recursive association operations before performing calculations. However, this serial processing method of dynamically executing organizational relationship resolution and global data scanning at query time has at least the following drawbacks: A. Due to the massive amount of detailed data and complex organizational relationships of enterprises, this method takes an extremely long time and cannot meet the requirements for real-time response.

[0025] B. This approach relies on a static, unchanging organizational structure model for data processing. When the company's organizational structure changes (such as departmental mergers, splits, or changes in reporting lines), the predefined data processing logic based on the old structure (such as fixed summary paths) immediately becomes invalid. To maintain query accuracy, developers must manually intervene and modify the data processing code. In other words, this approach is difficult to adapt to dynamic changes in the company's organizational structure.

[0026] In view of this, embodiments of this disclosure provide a data processing method oriented towards organizational structure. This method dynamically maintains a hierarchical bridging table including organizational relationships. Based on the hierarchical bridging table, detailed data associated with the finest-grained node is pre-positioned into the storage areas corresponding to all its ancestor nodes, thus pre-constructing a complete local dataset for each organizational node. In this way, subsequent query requests for any node can be processed directly based on the fully aggregated data in the storage area corresponding to that node, without needing to perform recursive traversal of the organizational tree and dynamic filtering of all data during the query. Compared to the serial processing of organizational relationship resolution and global data scanning that must be dynamically executed at the time of query in related technologies, this disclosure shifts the high-overhead processing stage of organizational relationship resolution and global data scanning to be completed concurrently at the time of data entry, providing a data localization foundation for subsequent queries. This reduces the time complexity of subsequent query responses from the aggregation computation complexity that originally increased quadratically with the number of nodes to near linear logarithmic levels, thereby achieving an order-of-magnitude improvement in response efficiency.

[0027] The technical solutions provided by the embodiments of this disclosure will now be described in conjunction with the accompanying drawings.

[0028] For example, Figure 1 This disclosure provides a data processing method oriented towards organizational structure. The executing entity of this method can be an electronic device, such as a terminal or a server. The specific executing entity can be determined according to the actual scenario. Figure 1 As shown, the method may include steps S101-S102.

[0029] S101. Obtain detailed data associated with the finest-grained nodes of the organizational structure.

[0030] In an organizational structure, each node can be called an organizational node, and these nodes are connected through reporting or jurisdictional relationships. The organizational structure includes at least one path from the root node (e.g., the group headquarters) to a leaf node (e.g., the most basic department or team). A leaf node can be understood as the finest-grained node, meaning it has no subordinate nodes. The root node can be any organizational node in the organizational structure except for the leaf nodes. For example, an organizational structure might include: Group, Business Unit A, Team B, and Employee C. Employee C has no subordinate nodes (i.e., the leaf node is Employee C), and the root node could be Group, Business Unit A, or Team B. A path contains only one root node.

[0031] Detailed data can be any data within the organizational structure that requires penetration processing. For example, detailed data can be one or more types of data requiring penetration processing, such as salary data, procurement data, production output, recruitment data, or resignation data. Salary data, for example, can include the salary data of a specific employee for a specific month. In this embodiment, salary data is used as an example for illustrative purposes.

[0032] In one possible implementation, the server receives detailed data generated by the data source system in real time. For example, if the detailed data is payroll data, the data source system could be an Enterprise Resource Planning (ERP) system. The ERP system can then send the generated detailed data to the server in real time.

[0033] S102. Based on the pre-established and maintained organizational structure hierarchical bridging table, the detailed data is pre-set to the dedicated data storage area corresponding to each ancestor node of the finest granularity node, so that the dedicated data storage area corresponding to any ancestor node includes all the detailed data belonging to the ancestor node in the organizational structure; the organizational structure hierarchical bridging table includes: the path relationship from each finest granularity node in the organizational structure to at least one ancestor node of the finest granularity node.

[0034] In this embodiment of the disclosure, the organizational structure hierarchical bridging table can be represented as a relationship table containing three core fields: child node identifier, ancestor node identifier, and hierarchical distance. The child node identifier field corresponds to a finest-grained organizational node, the ancestor node identifier field corresponds to a parent node of the child node in the organizational structure (including itself, its direct parent node, and all indirect parent nodes), and the hierarchical distance field represents the number of levels traversed from the child node to the ancestor node.

[0035] For example, for the finest-grained node "XX R&D Department", the organizational structure hierarchy bridging table can clearly express that: "XX R&D Department" is a direct subordinate level of "XX Company", with a hierarchy distance of 1; "XX R&D Department" is also a subordinate level of "YY Business Unit", with a hierarchy distance of 2; and "XX R&D Department" is also a subordinate level of "Group Headquarters" (i.e., the root node), with a hierarchy distance of 3. That is, the organizational structure hierarchy bridging table records the path relationships from the leaf node (i.e., the finest-grained node) in the organizational tree to all its ancestor nodes.

[0036] The dedicated data storage area can be understood as a data storage space independently allocated or logically partitioned for each organizational node in the organizational structure (including the root node, intermediate nodes, and the finest-grained nodes). This storage area logically serves its corresponding organizational node exclusively, centrally storing all detailed data belonging to that node.

[0037] In this embodiment of the disclosure, the dedicated data storage area corresponding to any ancestor node includes all detailed data belonging to that ancestor node in terms of organizational structure. This can be understood as the dedicated data storage area corresponding to the ancestor node including detailed data associated with all subordinate nodes of that ancestor node, as well as detailed data directly associated with that ancestor node. For example, "Company XX" has two subordinate nodes, "XX1 R&D Department" and "XX2 R&D Department," and "Employee AA," which directly belongs to the node "Company XX" but not to any subordinate node of "Company XX." Therefore, the dedicated data storage area corresponding to "Company XX" may include detailed data associated with "XX1 R&D Department," detailed data associated with "XX2 R&D Department," and detailed data associated with "Employee AA."

[0038] In one possible implementation, based on a pre-established and maintained organizational structure hierarchy bridging table, links pointing to detailed data associated with subordinate nodes of each ancestor node are created in the logical storage directory corresponding to that ancestor node. This allows subsequent query operations to retrieve the data to be processed by accessing these links, providing data conditions to shorten subsequent query request response times while also avoiding the storage overhead of multiple copies. In another possible implementation, a separate data list is maintained for each organizational node. Based on a pre-established and maintained organizational hierarchy bridging table, the unique identifier (such as an ID) of the detailed data associated with the finest-grained node is appended to the data lists of all ancestor nodes of that finest-grained node. This effectively shortens the response time of subsequent query requests. For example, subsequent query operations can first read the target node's list to obtain the ID list, and then quickly retrieve the corresponding detailed data from the central storage based on the ID, avoiding the need for subsequent query operations to search and retrieve the required detailed data from the global detailed data.

[0039] In another possible implementation, the detailed data is copied to the physical or logical storage area corresponding to each ancestor node of the finest-grained node.

[0040] For example, after obtaining the salary data of any finest-grained node, multiple complete salary data copies are generated based on the list of all ancestor nodes calculated from the organizational structure hierarchy bridging table. Each salary data copy is then stored in the physical or logical storage area corresponding to each of the ancestor nodes of that finest-grained node. Each salary data copy is a complete copy of the original data, ensuring that the physical or logical storage area corresponding to any node physically contains the salary data of all its subordinate nodes. This provides localized data conditions for subsequent query requests, thereby shortening the response time of candidate query requests. Furthermore, subsequent query operations can be performed directly on this salary data copy. Compared to querying links to detailed data associated with subordinate nodes of ancestor nodes or the data list of ancestor nodes, directly querying this salary data copy further shortens the response time of query requests.

[0041] In this embodiment, an organizational hierarchy bridging table, including organizational relationships, is dynamically maintained. Based on this table, detailed data associated with the finest-grained node is pre-stored in the dedicated data storage areas corresponding to each of its ancestor nodes, thus pre-constructing a complete local dataset for each organizational node. This allows subsequent query requests for any node to be processed directly based on the data in the dedicated data storage area corresponding to that node, eliminating the need for recursive traversal of the organizational tree and dynamic filtering of all data during the query. Compared to related technologies that require the serial processing of dynamically executing organizational relationship resolution and global data scanning at the query time, this disclosure shifts this high-overhead processing stage of organizational relationship resolution and global data scanning to concurrent completion at the data entry time, providing a data localization foundation for subsequent queries. This reduces the time complexity of subsequent query responses from the original aggregation computation complexity, which increases quadratically with the number of nodes, to near-linear logarithmic levels, thereby achieving an order-of-magnitude improvement in response efficiency.

[0042] In some possible implementations, the method may further include: in response to a query request for a target node, performing a processing operation corresponding to the query request on the detailed data in the dedicated data storage area corresponding to the target node, and outputting the processing result; the target node is any node in the organizational structure.

[0043] For example, when a query request is received targeting a specific node (such as any node in an organizational structure like a group headquarters, a business unit, or a subsidiary), a dedicated data storage area corresponding to that target node can be determined based on a pre-established mapping relationship between organizational nodes and storage areas. This dedicated data storage area already contains pre-set detailed data belonging to that target node and its subordinate levels. Subsequently, the processing operations corresponding to the query request can be performed on the detailed data in this dedicated data storage area, such as aggregation, statistics, or conditional filtering. After processing, the results are output, which can be used for subsequent decision analysis.

[0044] In this embodiment of the disclosure, by performing the processing operation corresponding to the query request on the detailed data in the dedicated data storage area corresponding to the target node and outputting the processing result, fast and accurate data query and statistics of any node in the organizational structure are realized, supporting real-time decision-making and multi-layer penetration analysis. There is no need to perform complex real-time data association and traversal during the query, which significantly improves the query response efficiency.

[0045] In real-world business organizations, the detailed data generated during operations and management is often highly sensitive. However, related technologies often employ static encryption during storage, while the data still needs to be decrypted into plaintext for processing during computation and analysis. This results in data exposure risks during storage, transmission, or intermediate computation, failing to achieve true end-to-end security.

[0046] To overcome the above problems, in some possible implementations, the detailed data associated with the finest-grained node of the organizational structure is encrypted. Performing the processing operation corresponding to the query request on the detailed data in the dedicated data storage area corresponding to the target node may include: Based on the homomorphic encryption algorithm, the processing operation corresponding to the query request is performed on the detailed data in the dedicated data storage area.

[0047] In a possible implementation, the data source system can use a homomorphic encryption algorithm to homomorphically encrypt the generated detailed data before sending it to the server. Thus, the detailed data obtained by the server, associated with the finest-grained nodes of the organizational structure, is homomorphically encrypted. When the server receives a query request, it can invoke a homomorphic encryption computation library to directly perform supported homomorphic operations such as summation and averaging on the encrypted detailed data. The result is still ciphertext, which is only decrypted once before being finally displayed to authorized users.

[0048] In this embodiment of the disclosure, the detailed data is in an encrypted state, and the processing operations performed on the detailed data in the dedicated data storage area are also performed in an encrypted state. This ensures data security throughout the entire chain and end-to-end data security, and also significantly improves the query response speed under a large-scale tree-structured organizational architecture, enabling millisecond-level real-time penetrating analysis capabilities.

[0049] Optionally, for complex calculations that do not support homomorphic encryption, the data source system can perform tagging during the encryption process of detailed data. For example, corresponding encrypted tags are generated based on preset classification dimensions (such as organizational level or job category) and stored in association with the encrypted data. In subsequent queries, grouping, statistical, or filtering operations can be performed based on the encrypted tags of the detailed data in the dedicated data storage area without decrypting the original data content. This significantly improves query response speed under large-scale tree-structured organizational architectures while ensuring end-to-end data security, achieving millisecond-level real-time penetrating analysis capabilities.

[0050] In some other possible implementations, the detailed data associated with the finest-grained node of the organizational structure is encrypted. The processing operation corresponding to the query request is performed on the detailed data in the dedicated data storage area corresponding to the target node. This may include: for complex queries involving non-homomorphic operations such as sorting and fuzzy matching, restricted decryption may be performed in a trusted execution environment or hardware security module.

[0051] For example, when a query is triggered, the encrypted data and its corresponding computational tasks in the target node's dedicated data storage area can be scheduled to an isolated, hardware-trusted environment. Within this environment, the data is decrypted and computations are performed, and the results are re-encrypted before output. The entire process is invisible to external systems (including operating system administrators), thus limiting the exposure of plaintext data to a hardware-protected trusted boundary, thereby enhancing data security.

[0052] In some possible implementations, in response to a query request for a target node, the processing operation corresponding to the query request is performed on the detailed data in the dedicated data storage area corresponding to the target node, and the processing result is output, including: For any dedicated data storage area, perform pre-aggregation processing on the detailed data in the dedicated data storage area to obtain the pre-aggregation result; In response to a query request for a target node, the pre-aggregated result corresponding to the query request is used as the processing result.

[0053] In a possible implementation, after pre-setting detailed data into the dedicated data storage area corresponding to each ancestor node of the finest-grained node, the detailed data in each dedicated data storage area can be pre-aggregated to generate and store the corresponding pre-aggregation results. When a query request for a target node is received, it is not necessary to traverse or calculate all detailed data under that target node in real time. Instead, the pre-aggregation result matching the query request is directly obtained as the final processing result output. For example, if the pre-aggregation result of "SS Business Unit's total salary in May 2024" has been pre-calculated and stored, when a user queries this indicator, the system can directly return the pre-aggregation result without traversing or calculating all detailed data under the "SS Business Unit" node in real time. This can further achieve millisecond-level query response, significantly reduce the computational load of real-time queries, and support high-concurrency, high-frequency penetration analysis scenarios.

[0054] Optionally, the above pre-aggregated results can also be visualized through front-end interfaces such as management dashboards, report centers, or mobile applications, and can be presented in the form of charts, dashboards, real-time warning panels, or interactive drop-through analysis views.

[0055] In some possible implementations, the query request carries the time period to be queried; other methods include: Receive enterprise data; The organizational structure hierarchy bridging table is updated based on enterprise data. The organizational structure hierarchy bridging table carries a version identifier to indicate the effective time. In response to a query request for a target node, the system performs the corresponding processing operations on the detailed data in the dedicated data storage area corresponding to the target node, and outputs the processing results, including: In response to a query request for a target node, determine one or more versions of the organizational structure hierarchy bridging table that are in effect during the query period based on the version identifier; For each version of the organizational structure hierarchical bridging table, the target detailed data whose generation time is within the time period to be queried is determined from the dedicated data storage area corresponding to the target node; Perform the corresponding processing operation on the target detailed data and output the processing result.

[0056] Enterprise data may include data related to updates to the enterprise's organizational structure. For example, enterprise data may include information such as the DD department's parent company becoming CC company.

[0057] In a possible implementation, the child node identifiers, ancestor node identifiers, and hierarchical distances of the organizational structure hierarchy bridging table can be updated based on enterprise data to obtain the updated organizational structure hierarchy bridging table. Each organizational structure hierarchy bridging table can carry a version identifier to indicate the effective date. For example, the version identifier could be 2025.8.10, indicating that the organizational structure hierarchy bridging table carrying this version identifier was updated and became effective on August 10, 2025.

[0058] The query request carries the time period to be queried. Upon receiving a query request for a target node, it can determine which version identifiers correspond to organizational structure hierarchical bridging tables that are valid within the query period based on the version identifiers and the time period. This allows it to determine which detailed data in the dedicated data storage area corresponding to the target node was generated within the query period, thus identifying the target detailed data. Then, for any version of the organizational structure hierarchical bridging table that is valid within the query period, the processing operation corresponding to the query request is executed on the target data whose generation time was within the effective time of that version's organizational structure hierarchical bridging table, obtaining the processing result corresponding to that version's organizational structure hierarchical bridging table. Finally, based on the processing results corresponding to each version of the organizational structure hierarchical bridging table, the final processing result is obtained.

[0059] For example, the query request is to retrieve the total compensation of Company CC for the five months from August to December 2025. The query period is therefore the five months from August to December 2025. In August 2025, the version identifier of the organizational structure hierarchy bridging table used is 2025.6.1, and the version identifier prior to this is 2025.1.1. On September 1, 2025, the organizational structure hierarchy bridging table was updated, and the updated version identifier is 2025.9.1. For example, the update might be that the parent company of the DD department changed from BB company to CC company, and CC company changed from having only one department (FF) to having both DD and FF departments. Therefore, when the target node is Company CC, the organizational structure hierarchy bridging tables effective during the query period are the organizational structure hierarchy bridging tables corresponding to version identifiers 2025.6.1 and 2025.9.1, respectively. For the organizational structure bridging table corresponding to version 2025.6.1, the target detail data associated with the FF department in August 2025 is determined from the dedicated receipt storage area corresponding to the CC company target node, and the total compensation of the FF department in August 2025 is calculated based on this target detail data. For the organizational structure bridging table corresponding to version 2025.9.1, the target detail data of the FF and DD departments from September to December 2025 is determined from the dedicated receipt storage area corresponding to the CC company target node, and the total compensation of the FF and DD departments from September to December 2025 is calculated based on this target detail data. Then, based on the total compensation of the FF and DD departments from September to December 2025 and the total compensation of the FF department in August 2025, the total compensation of CC company for the five months from August to December 2025 is obtained, which is the final processing result.

[0060] In this embodiment, the organizational structure hierarchy bridging table can be dynamically updated according to changes in the enterprise's organizational structure, and a version identifier with the effective time is recorded with each update. When a query request carrying a time period to be queried is received, one or more bridging table versions effective within that time period can be matched, and detailed data generated within the corresponding time period can be filtered from the dedicated data storage area corresponding to the target node based on the organizational relationship corresponding to each version. In this way, it can be ensured that no matter how the organizational structure changes, the statistical results of historical data during the query are always consistent with the organizational hierarchy of the corresponding period without adjusting the data processing code. This avoids the deviation of historical data attribution caused by structural changes, thereby improving the accuracy and business adaptability of detailed data penetration analysis in the time series dimension. Moreover, this method does not require developers to manually intervene and modify the data processing code, which can effectively save manpower and time costs.

[0061] To more clearly describe the technical solutions provided in the embodiments of this disclosure, please refer to [link / reference]. Figure 2 .

[0062] For example, Figure 2 This is a schematic diagram of a data processing system architecture oriented towards organizational structure, provided as an embodiment of this disclosure.

[0063] like Figure 2 As shown, the data source system generates detailed data and encrypts the generated detailed data using a preset encryption algorithm to obtain encrypted detailed data. For example, the preset encryption algorithm can be at least one of the following: symmetric encryption, asymmetric encryption, and hash algorithms, etc.

[0064] The data source system sends encrypted detailed data to the data warehouse through streaming pipelines.

[0065] The operational data store (ODS) of a data warehouse can be used to store encrypted detailed data.

[0066] The data warehouse detail (DWD) layer is used to preprocess the detailed data from the ODS layer to improve its accuracy. For example, preprocessing may include data cleaning and standardization.

[0067] In a possible implementation, the detailed data can be scheduled to a trusted environment for decryption, followed by data cleaning, standardization, and other processing. The processed detailed data can then be encrypted and returned.

[0068] The data warehouse middle (DWM) layer can be used to dynamically maintain the organizational structure hierarchy bridging tables, and based on the organizational structure hierarchy bridging tables, to pre-set detailed data into the dedicated data storage area corresponding to each ancestor node of the finest-grained node of the organizational structure.

[0069] The data warehouse summary (DWS) layer can be used to pre-aggregate detailed data in the dedicated data storage areas corresponding to each organizational node, obtaining pre-aggregated results. Since the detailed data has been pre-allocated according to paths, aggregation becomes a simple local computation, thereby improving processing efficiency by orders of magnitude.

[0070] The application data service (ADS) layer of the data warehouse can be used to store pre-aggregated results and also to send pre-aggregated results to the three-stage linkage decision analysis center.

[0071] The pre-aggregated results sent to the three-stage linkage decision analysis center can be encrypted text. Alternatively, they can be core indicators that have been decrypted after security approval, such as indicators that have been anonymized or whose sensitivity has been reduced after aggregation, such as "percentage increase in average departmental salary" and "salary cost ratio".

[0072] The three-terminal linkage decision analysis center includes a management dashboard for strategic decision-making, a multi-dimensional analysis system for management analysis, and a process processing interface for business operations. The management dashboard, as the core component of strategic decision-making, provides a group-wide panoramic view of compensation, real-time alerts, trend forecasts, and drill-down functionality, enabling senior managers to monitor human resource cost dynamics and anomalies in real time.

[0073] In this embodiment, the system employs a "computation is encryption" security architecture to achieve end-to-end encrypted data operations from source encryption, encrypted transmission, storage, to computational processing, completely avoiding the data exposure risks caused by decryption during the computation stage in traditional solutions. Addressing the performance bottleneck of real-time penetration aggregation under unbalanced tree-structured organizational architectures, the system innovatively designs a five-layer data warehouse architecture and a dynamic hierarchical bridging table mechanism. This reduces the time complexity of cross-node aggregation computation from the traditional quadratic growth of the number of nodes to near-linear logarithmic levels (e.g., optimizing the time complexity of cross-node aggregation computation from the traditional O(n²) to approximately O(n log n)), significantly improving the efficiency of large-scale organizational penetration queries. This supports millisecond-level real-time penetration queries and source tracing analysis even at organizational node scales of tens of thousands or even hundreds of thousands. Through a stream-batch integrated processing engine and high-speed data channels, the system achieves high-concurrency data ingestion and real-time processing capabilities of millions of encrypted data entries per second, accommodating both real-time monitoring and batch analysis scenarios. Building upon this foundation, a three-tiered decision analysis center, constructed using a unified backend computing engine, provides differentiated and collaborative data services to the strategic decision-making, management analysis, and business operation levels. This drives the transformation of payroll management from post-event statistics to a closed-loop decision-making system that integrates in-process early warning and pre-event prediction. This architecture boasts excellent system compatibility and business scalability, flexibly adapting to various data sources, including ERP systems, and dynamically adjusting with organizational structure to support the data governance and decision analysis needs of enterprises throughout their continuous development.

[0074] For example, Figure 3 This is a schematic diagram of a data processing apparatus oriented towards organizational structure, provided as an embodiment of this disclosure. Figure 3 As shown, the data processing device 30 for organizational structures includes an acquisition module 301 and a preset module 302, wherein, The acquisition module 301 is used to acquire detailed data associated with the finest-grained nodes of the organizational structure; The pre-configured module 302 is used to pre-configure detailed data into the dedicated data storage area corresponding to each ancestor node of the finest granularity node according to the pre-established and maintained organizational structure hierarchical bridging table, so that the dedicated data storage area corresponding to any ancestor node includes all detailed data belonging to the ancestor node in the organizational structure; the organizational structure hierarchical bridging table includes: the path relationship from each finest granularity node in the organizational structure to at least one ancestor node of the finest granularity node.

[0075] In one possible implementation, the preset module 302 is specifically used for: The detailed data is copied to the physical or logical storage area corresponding to each ancestor node of the finest-grained node.

[0076] In one possible implementation, the device 30 further includes: The query module is used to respond to query requests for a target node, perform the corresponding processing operations on the detailed data in the dedicated data storage area corresponding to the target node, and output the processing results; the target node can be any node in the organizational structure.

[0077] In one possible implementation, detailed data associated with the finest-grained nodes of the organizational structure is encrypted; the query module is specifically used for: Based on the homomorphic encryption algorithm, the processing operation corresponding to the query request is performed on the detailed data in the dedicated data storage area.

[0078] In one possible implementation, the query module is specifically used for: For any dedicated data storage area, perform pre-aggregation processing on the detailed data in the dedicated data storage area to obtain the pre-aggregation result; In response to a query request for a target node, the pre-aggregated result corresponding to the query request is used as the processing result.

[0079] In one possible implementation, the query request carries a time period to be queried, and the device 30 further includes: The receiving module is used to receive enterprise data; The update module is used to update the organizational structure hierarchy bridging table based on enterprise data. The organizational structure hierarchy bridging table carries a version identifier to indicate the effective time. The query module is specifically used for: In response to a query request for a target node, determine one or more versions of the organizational structure hierarchy bridging table that are in effect during the query period based on the version identifier; For each version of the organizational structure hierarchical bridging table, the target detailed data whose generation time is within the time period to be queried is determined from the dedicated data storage area corresponding to the target node; Perform the corresponding processing operation on the target detailed data and output the processing result.

[0080] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0081] Figure 4 This is a block diagram of an electronic device provided according to an embodiment of the present disclosure. For example, electronic device 400 may be provided as a server. (Refer to...) Figure 4 The electronic device 400 includes a processor 422, which may be one or more, and a memory 432 for storing computer programs executable by the processor 422. The computer program stored in the memory 432 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 422 may be configured to execute the computer program to perform the aforementioned organizational-oriented data processing method.

[0082] Additionally, the electronic device 400 may also include a power supply component 426 and a communication component 450. The power supply component 426 may be configured to perform power management of the electronic device 400, and the communication component 450 may be configured to enable communication of the electronic device 400, such as wired or wireless communication. Furthermore, the electronic device 400 may also include an input / output (I / O) interface 458. The electronic device 400 can operate on an operating system stored in the memory 432.

[0083] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the organizational structure-oriented data processing method described above. For example, the non-transitory computer-readable storage medium may be the memory 432 including the program instructions described above, which may be executed by the processor 422 of the electronic device 400 to complete the organizational structure-oriented data processing method described above.

[0084] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described organizational structure-oriented data processing method when executed by the programmable device.

[0085] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0086] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0087] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A data processing method oriented towards organizational structure, characterized in that, The method includes: Retrieve detailed data associated with the finest-grained nodes of the organizational structure; Based on the pre-established and maintained organizational structure hierarchical bridging table, the detailed data is pre-set into the dedicated data storage area corresponding to each ancestor node of the finest granularity node, so that the dedicated data storage area corresponding to any ancestor node includes all detailed data belonging to the ancestor node in the organizational structure; the organizational structure hierarchical bridging table includes: the path relationship from each finest granularity node in the organizational structure to at least one ancestor node of the finest granularity node.

2. The method according to claim 1, characterized in that, The step of presetting the detailed data into the dedicated data storage area corresponding to each ancestor node of the finest-grained node includes: The detailed data is copied to the physical or logical storage area corresponding to each ancestor node of the finest-grained node.

3. The method according to claim 1 or 2, characterized in that, The method further includes: In response to a query request for a target node, the processing operation corresponding to the query request is performed on the detailed data in the dedicated data storage area corresponding to the target node, and the processing result is output; the target node is any node in the organizational structure.

4. The method according to claim 3, characterized in that, The detailed data associated with the finest-grained node of the organizational structure is encrypted; the processing operation corresponding to the query request is performed on the detailed data in the dedicated data storage area corresponding to the target node, including: Based on the homomorphic encryption algorithm, the processing operation corresponding to the query request is performed on the detailed data in the dedicated data storage area.

5. The method according to claim 3, characterized in that, In response to a query request for a target node, the system performs the processing operation corresponding to the query request on the detailed data in the dedicated data storage area corresponding to the target node, and outputs the processing result, including: For any dedicated data storage area, the detailed data in the dedicated data storage area is pre-aggregated to obtain the pre-aggregation result; In response to a query request for the target node, the pre-aggregated result corresponding to the query request is used as the processing result.

6. The method according to claim 3, characterized in that, The query request carries the time period to be queried, and the method further includes: Receive enterprise data; The organizational structure hierarchy bridging table is updated based on the enterprise data, and the organizational structure hierarchy bridging table carries a version identifier to indicate the effective time; In response to a query request for a target node, the system performs the processing operation corresponding to the query request on the detailed data in the dedicated data storage area corresponding to the target node, and outputs the processing result, including: In response to a query request for the target node, one or more versions of the organizational structure hierarchy bridging table that are in effect during the query time period are determined based on the version identifier. For each version of the organizational structure hierarchical bridging table, the target detailed data generated within the time period to be queried is determined from the dedicated data storage area corresponding to the target node. Perform the processing operation corresponding to the query request on the target detailed data and output the processing result.

7. A data processing device oriented towards organizational structure, characterized in that, The device includes: The acquisition module is used to retrieve detailed data associated with the finest-grained nodes of the organizational structure; A pre-configured module is used to pre-configure the detailed data into the dedicated data storage area corresponding to each ancestor node of the finest-grained node according to a pre-established and maintained organizational structure hierarchical bridging table, so that the dedicated data storage area corresponding to any ancestor node includes all detailed data belonging to the ancestor node in the organizational structure; the organizational structure hierarchical bridging table includes: the path relationship from each finest-grained node in the organizational structure to at least one ancestor node of the finest-grained node.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-6.

9. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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