Automatic numerical value determination method and device based on hierarchical information, equipment and medium

Abstract

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses an automatic numerical value determination method and device based on hierarchical information, equipment and a medium. Comprising the following steps: establishing and storing service level information, target value determination parameters, a transaction data structure template, a target value result presentation template and associated object level information; receiving the original transaction data and performing formatting conversion to generate standardized transaction data; generating initial target value details based on the standardized transaction data and the configuration information; identifying details of abnormal target values; target value summary information is generated for the initial target value details which are not marked as abnormal; target value summary information is transmitted and feedback information is received. Through a standardized transaction data structure and a unified target value configuration system, target value affiliation and numerical value distribution between cross-service units and associated objects are realized; and flexible summarization of results is supported through a convergence template, so that the automation level of target value processing is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, equipment and storage medium for automatically determining a numerical value based on hierarchical information. Background Art

[0002] In the internal management of large group companies, the expense settlement system is a key support mechanism for achieving rational resource allocation, cost transparency, and operational efficiency evaluation. Especially in cross-subsidiary and cross-business unit operating models, the division of financial responsibilities between service providers and beneficiaries needs to be achieved through a refined internal billing system. Currently, there are some billing products or solutions on the market that target a single service type. These usually support billing based on usage (such as CPU time, storage capacity, network bandwidth, etc.) and have a certain degree of automation and traceability at the resource level. This type of solution is widely used in standardized IaaS resource usage scenarios, but its versatility and flexibility are still insufficient when facing the complex management needs of group companies.

[0003] In the fintech sector, multiple business lines share a unified data processing platform, risk control engine, or transaction matching service. Due to varying degrees of reliance on the same platform by different business departments and frequent adjustments to business development, existing billing systems based on static rule configurations struggle to dynamically map actual service usage. This leads to uneven cost allocation, distorted cost accounting, and impacts the optimization and scheduling of internal resources and the accuracy of financial accounting.

[0004] In the healthcare sector, hospital groups or health management companies often integrate multiple independent institutions to share imaging diagnostic platforms, intelligent consultation systems, or health assessment services. The proportion of benefits benefiting each institution varies significantly, and existing technologies are generally unable to configure a detailed target allocation structure for different levels of service users (such as departments, hospital districts, and physician groups). They also lack target value attribution logic for multi-level linked objects, resulting in a settlement process that relies on manual configuration, is prone to errors, and is inefficient.

[0005] Furthermore, most current systems suffer from significant shortcomings in rule adaptability, data fusion capabilities, and real-time processing. Existing billing systems typically utilize fixed templates to drive rule execution, making it difficult to respond promptly to changes in service types or adjustments to business policies. This makes them inadequate for scenarios with multiple service types and the flexibility to adjust target value structures. Furthermore, because underlying data sources are spread across multiple service platforms and lack a unified data structure and aggregation mechanism, billing input data suffers from consistency and integrity issues, impacting target value attribution and accounting accuracy.

[0006] In summary, the existing internal billing system has obvious limitations in dealing with multiple service types, multiple object levels, dynamic business adjustments, etc., and it is difficult to support the automated allocation and settlement needs of structured target values ​​under complex organizational structures. Summary of the Invention

[0007] The main purpose of the present invention is to provide a method, device, equipment and storage medium for automated numerical determination based on hierarchical information, aiming to solve the technical problem that the existing technology cannot realize the automated attribution and aggregation processing of structured target values ​​for multiple service types, multi-related object hierarchical structures and dynamic adjustment requirements.

[0008] To achieve the above object, the present invention provides an automatic value determination method based on hierarchical information, comprising:

[0009] Establish and store service hierarchy information, target value determination parameters, transaction data structure templates, target value result presentation templates, and associated object hierarchy information;

[0010] Receiving raw transaction data to be processed, and formatting and converting the raw transaction data according to the transaction data structure template to generate standardized transaction data;

[0011] In response to the target value processing trigger instruction, based on the standardized transaction data and in combination with the service level information, the target value determination parameters and the associated object level information, target value attribution and target value allocation processing are performed to generate initial target value details;

[0012] Detect whether there is any data configuration missing or data logic conflict in the initial target value details. If so, mark the initial target value details containing the data configuration missing or data logic conflict as abnormal target value details and output them;

[0013] For the initial target value details that are not marked as abnormal target value details, generate target value summary information according to the target value result presentation template;

[0014] The target value summary information is transmitted to a preset target value recording target, and reception status feedback information of the target value recording target is received.

[0015] Furthermore, to achieve the above-mentioned object, the present invention provides an automatic value determination device based on hierarchical information, comprising:

[0016] Configuration management module, used to establish and store service hierarchy information, target value determination parameters, transaction data structure templates, target value result presentation templates and associated object hierarchy information;

[0017] A data access module is used to receive the original transaction data to be processed, and format and convert the original transaction data according to the transaction data structure template to generate standardized transaction data;

[0018] a target value calculation module, configured to respond to a target value processing trigger instruction, perform target value attribution and target value allocation processing based on the standardized transaction data and in combination with the service level information, the target value determination parameters, and the associated object level information, and generate an initial target value detail;

[0019] an abnormality identification module, configured to detect whether there is any data configuration missing or data logic conflict in the initial target value details; if so, mark the initial target value details containing the data configuration missing or data logic conflict as abnormal target value details and output the result;

[0020] A result summary module is used to generate target value summary information based on the target value result presentation template for the initial target value details that are not marked as abnormal target value details;

[0021] The result push module is used to transmit the target value summary information to a preset target value recording target and receive reception status feedback information of the target value recording target.

[0022] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a determination machine device, which includes a memory, a processor, and an automatic numerical determination program based on hierarchical information stored in the memory and runnable on the processor. When the automatic numerical determination program based on hierarchical information is executed by the processor, the steps of the automatic numerical determination method based on hierarchical information as described above are implemented.

[0023] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a determination machine-readable storage medium, on which is stored an automated numerical determination program based on hierarchical information. When the automated numerical determination program based on hierarchical information is executed by a processor, the steps of the automated numerical determination method based on hierarchical information as described above are implemented.

[0024] Beneficial effects: The present invention relates to the field of data processing technology and can be applied to business scenarios such as financial technology and medical health. It discloses an automated numerical value determination method, device, equipment and medium based on hierarchical information, including: establishing and storing service hierarchical information, target value determination parameters, transaction data structure templates, target value result presentation templates and associated object hierarchical information; receiving original transaction data and performing format conversion to generate standardized transaction data; generating initial target value details based on standardized transaction data and configuration information; identifying abnormal target value details and outputting them; generating target value summary information for initial target value details that are not marked as abnormal; transmitting target value summary information and receiving feedback information. The present invention realizes target value attribution and numerical value allocation across service units and associated objects through standardized transaction data structure and a unified target value configuration system; ensures data quality through an abnormality identification mechanism; supports flexible aggregation and docking of results through aggregation templates, thereby improving the accuracy, adaptability and automation level of target value processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0026] Figure 1 A schematic diagram of an application environment of an automatic value determination method based on hierarchical information in an embodiment of the present invention;

[0027] Figure 2 1. A flow chart of an embodiment of a method for automatically determining a numerical value based on hierarchical information according to the present invention;

[0028] Figure 3 Schematic diagram of functional modules of a preferred embodiment of an automatic value determination device based on hierarchical information of the present invention;

[0029] Figure 4 A schematic structural diagram of a determination device according to an embodiment of the present invention;

[0030] Figure 5 FIG. 2 is another structural diagram of a determination device in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0032] The automatic value determination method based on hierarchical information provided by the embodiment of the present invention can be applied in the following situations: Figure 1In an application environment, the user end communicates with the server end through a network. The server end can establish and store service hierarchy information, target value determination parameters, transaction data structure templates, target value result presentation templates, and associated object hierarchy information through the user end; receive original transaction data and perform format conversion to generate standardized transaction data; generate initial target value details based on the standardized transaction data and configuration information; identify abnormal target value details and output them; generate target value summary information for initial target value details that are not marked as abnormal; transmit target value summary information and receive feedback information. The present invention realizes target value attribution and numerical allocation across service units and associated objects through standardized transaction data structure and unified target value configuration system; ensures data quality through an abnormality identification mechanism; supports flexible aggregation and docking of results through aggregation templates, thereby improving the accuracy, adaptability, and automation level of target value processing. The user end can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The server end can be implemented by an independent server or a server cluster consisting of multiple servers. The present invention is described in detail below through specific embodiments.

[0033] See also Figure 2 , Figure 2 This is a flow chart of an embodiment of the method for automatically determining a numerical value based on hierarchical information provided by the present invention. It should be noted that although a logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0034] like Figure 2 As shown, the automatic value determination method based on hierarchical information proposed by the present invention includes the following steps:

[0035] S10, establishing and storing service hierarchy information, target value determination parameters, transaction data structure template, target value result presentation template and associated object hierarchy information;

[0036] In this embodiment, establishing and storing service hierarchy information refers to constructing a set of data structures with hierarchical relationships based on the division of responsibilities, organizational structure and settlement rights of different service units in the business system. Service hierarchy information may include multi-level units such as group headquarters, regional branches, business subsidiaries, service teams, etc., and the ownership relationship and settlement path between each unit are represented by a tree structure or a graph structure. In actual implementation, the unique identification, superior relationship mapping, and affiliated functional label of the service unit can be extracted by parsing the enterprise organizational structure document, system permission table or contract template information, and stored in a structured database to support the service ownership determination of subsequent processing procedures. This structure can be implemented based on a table in a relational database, or a graph database can be constructed for multi-layer penetration query.

[0037] Target value determination parameters define the constraints and reference standards for target value calculation, attribution, allocation, and tracking. These parameters may include target value calculation formulas, allocation coefficients, reference indicator mapping rules, applicable target categories for different service units, time period constraints, and adjustable tolerance limits. In actual implementation, these parameters can be defined using configuration files, parameter template tables, or rule engine configuration items, and stored in the parameter management module. The system automatically loads this parameter set as the input condition source before executing target value generation.

[0038] The transaction data structure template serves as a blueprint for standardized business data modeling, unifying the format of raw transaction data from various sources. The template structure defines field names, field types, logical relationships between fields, and the business domain to which the fields belong, such as amount, transaction time, business line, and billing entity. Transaction data structure templates can be created through manual definition combined with data dictionary extraction, or by analyzing and extracting key fields from historical transaction data. Templates are typically stored in the data model management module in JSON or XML format and include version control.

[0039] The target value result presentation template specifies the output organization and display format for target value attribution results. This template defines the result aggregation dimensions, field display order, display labels, grouping logic, and output format, such as displaying the total target value by service tier and the attribution percentage by object tier. This can be implemented as a configurable template file or a display structure definition generated by a low-code visualization tool, combined with exportable CSV, Excel, and API structures for flexible integration.

[0040] Associated object hierarchical information refers to the structure of objects other than service units that participate in target value attribution, such as the information structure of roles like cost bearers, business initiators, and consumer users. These objects may originate from different business systems and have independent numbering systems and hierarchical attributions. Mapping rules are needed to unify these objects into the target value attribution system. This can be achieved through unified management of object hierarchical relationships within an ID mapping table, role dimension model, or master data platform. Data cleansing mechanisms can be introduced to eliminate duplicate and conflicting information to ensure accurate target attribution.

[0041] Once this information is established, it needs to be stored in a data management module that ensures high availability and consistency. A distributed relational database can be used to centrally manage structured data, while also integrating metadata management mechanisms to record field definitions, source descriptions, and version evolution. To ensure real-time accessibility of configuration data, a caching mechanism or interface aggregation service can be established to load data on demand during system operation, improving processing efficiency and ensuring data consistency.

[0042] In a certain implementation, service hierarchy information can be generated by automatically parsing the enterprise organizational structure table, supporting the docking of the organizational relationship map in the OA system or human resources system, and automatically converting the department code and attribution relationship into a hierarchical model that can be used for target value allocation. The target value determination parameters can be set by the business rule configuration platform, and different business parties input their own allocation rules and attribution formulas. The definition of the transaction data structure template can automatically generate field rules through the data modeling tool, and support customized extension fields to adapt to the data input format of different systems. The result presentation template can be generated by the visual configuration interface, allowing users to freely combine fields and display levels. The associated object hierarchy information can be synchronized with the group's unified master data platform, and the object mapping table in the master data can be used to unify the object numbers between different systems to avoid attribution errors caused by inconsistent IDs.

[0043] Another implementation approach involves using a graph database like Neo4j to store the service hierarchy and associated object hierarchy, enabling complex relationship resolution through graph traversal. Parameter definitions can be flexibly controlled through a rules engine platform like Drools, dynamically enabling or disabling certain parameter entries based on business needs. Result templates can be integrated with BI systems to automatically generate permission-controlled result dashboards, displaying different summary perspectives based on user identity.

[0044] This configuration information can also be exported as a unified configuration package and deployed to different subsystems via file transfer or interface, achieving unified target value processing configuration synchronization across multiple systems. To improve configuration efficiency, a pre-built standard template library supports rapid loading of standard configurations by industry, business scenario, or company type, and enables dynamic adjustment and version tracking based on real-time feedback.

[0045] Example: In the healthcare business, to meet the need for medical resource cost aggregation within a regional hospital group, service-level information can be established to represent the hierarchical relationship between the group, central hospitals, and community medical institutions. Target value determination parameters are used to set resource usage ratio rules for each level of institutions. The transaction data structure template defines a unified field structure for various types of medical expenses, such as hospitalization, outpatient, and examinations. The result presentation template is used to display the attributable amount by department and patient category. The associated object hierarchical information includes the corresponding levels of departments, doctors, and patients. This processing flow enables the structured presentation and regulatory traceability of cost attribution.

[0046] In the fintech sector, to address the target allocation requirements for public technology service fees across different business departments within a bank, service-level information can be constructed to map different product lines and channel departments. Target value determination parameters set target value weighting factors based on transaction volume, number of users, service call frequency, and other dimensions. A transaction data structure template integrates service call logs and transaction details from different systems. The target value result presentation template outputs target allocation data by month and business line. The associated object hierarchy includes information on branches, account groups, and channel agency structures. This process ensures transparent and compliant allocation, effectively supporting internal expense management decisions.

[0047] By establishing and storing service hierarchical information, target value determination parameters, transaction data structure templates, target value result presentation templates, and associated object hierarchical information, this embodiment provides a structured, configurable, and scalable infrastructure for target value attribution and value assignment, enabling standardized processing of input data and unified presentation of output data. This mechanism ensures the accuracy of attribution logic while supporting flexible adaptation to diverse business scenarios and organizational structures, enhancing the automation level of the target value processing process and the transparency and controllability of the results.

[0048] S20, receiving raw transaction data to be processed, and formatting and converting the raw transaction data according to the transaction data structure template to generate standardized transaction data;

[0049] In this embodiment, receiving raw transaction data to be processed refers to extracting transaction information related to target value attribution from multiple business systems or data sources. These raw transaction data may come from different subsidiaries, business units or third-party systems, with different formats, field naming and data types, and usually exist in structured (such as database tables), semi-structured (such as CSV, JSON) or unstructured (such as log files) forms. In order to achieve unified processing, it is necessary to establish a unified data access interface that can support multiple data input methods such as batch import, streaming processing or interface call.

[0050] A transaction data structure template is a structural definition used to convert received raw transaction data into a unified format and perform semantic mapping. This template typically includes standard field names, field type definitions, inter-field dependencies, time window requirements, data integrity constraints, and business semantic annotations. The template's purpose is to map raw data fields with inconsistent formats and semantics into standard, structured fields that the system can identify, ensuring that data from different sources can be attributed and calculated under the same rules. Transaction data structure templates can be created through the metadata management platform and support versioning and field extensions.

[0051] Formatting and converting raw transaction data according to a transaction data structure template involves organizing the raw data into a data set with a clear structure, consistent semantics, and that meets business rule requirements through methods such as data cleansing, field mapping, data type conversion, missing value completion, and outlier processing. The specific conversion process includes: extracting corresponding data against template fields; performing forced conversion or logical mapping (such as monetary unit conversion and date format standardization) on fields with inconsistent data types; filling missing fields with default values ​​or inference rules; and marking or removing abnormal fields according to template rules. The conversion process is generally performed by an ETL (Extract-Transform-Load) task or a data processing engine, and marking the processing status of each record after processing is completed.

[0052] Generating standardized transaction data means, after formatting and conversion, forming a unified dataset that can be used for subsequent target value attribution and calculation. Standardized transaction data features complete field structure, consistent data semantics, clear processing status, and direct participation in target value calculation and attribution logic execution. This dataset is used in the target value processing module for allocation, comparison, and verification, and supports subsequent data visualization and result export. To ensure the traceability of standardization results, the system records a mapping log between original and standardized data, facilitating subsequent audits and problem identification.

[0053] In one implementation, raw transaction data can be captured in real time from the business database through an interface connection, such as capturing expense records from the financial system and call detail logs from the service gateway system. Business personnel configure the transaction data structure template through a configuration interface within the system, setting field mapping relationships and field validation rules. The system automatically generates the template structure and records it as a structured configuration item. After receiving the raw data, the data processing engine performs operations such as field extraction, unit conversion, timestamp standardization, redundant field removal, and abnormal record marking according to the template, writing the results to a standardized transaction data table. The data table supports indexing by time, service unit, and object dimensions, facilitating subsequent queries and aggregation operations.

[0054] In another implementation, the system supports streaming data processing. Raw transaction data is transmitted as a data stream via Kafka or Flume and enters the data cleansing engine. Transaction data structure templates are stored in a template repository in JSON format. The data cleansing engine loads the corresponding template rules from this repository and performs real-time structure mapping and data validation on each data stream item. The resulting standardized transaction data is directly input into an in-memory database or cache structure through a data channel to support high-frequency attribution logic calculation and feedback generation.

[0055] It also implements source tracing and quality assessment mechanisms for received raw transaction data. For example, it assigns a source tag and access timestamp to each data record; establishes field-level quality scoring rules to assess missing rates, anomaly rates, and duplication rates, and dynamically adjusts field importance and processing priority. It also supports retroactive conversion of historical data when switching between template versions, ensuring consistent data processing throughout the entire lifecycle.

[0056] Example: In the healthcare business, raw transaction data can come from hospital information systems (HIS), fee settlement systems, and telemedicine platforms, including records of fees for registration, medical treatment, medication, and examinations. These data structures and field names are not uniform, and there may be duplication from multiple sources, inconsistent amount units, or missing record information. By loading a transaction data structure template, the system integrates data from different department systems into a unified format, generating standardized transaction data containing fields such as service time, service type, fee amount, and service recipient, providing basic data support for subsequent target value attribution and cost allocation processing.

[0057] In the fintech business sector, raw transaction data may come from payment platforms, account systems, risk management systems, and user behavior logs. This data has varying field granularity, format, and source systems. The system uses standardized templates to unify data from these systems into a unified transaction structure, including transaction number, transaction amount, service type, channel source, and account identifier. This creates a dataset that can be used for service fee attribution, performance target aggregation, and multi-party reconciliation, achieving unified target value processing across multiple systems.

[0058] This embodiment receives the original transaction data to be processed and completes formatting conversion based on a unified transaction data structure template, which can effectively eliminate the differences in multi-source data structures and semantic inconsistencies, generate standardized transaction data that meets the attribution and allocation calculation requirements, and ensure the consistency, accuracy and traceability of data processing, thereby supporting the stable operation of subsequent complex target value processing processes.

[0059] S30, in response to the target value processing trigger instruction, performing target value attribution and target value allocation processing based on the standardized transaction data and in combination with the service level information, the target value determination parameters, and the associated object level information to generate initial target value details;

[0060] In this embodiment, responding to a target value processing trigger instruction means that the system, upon receiving an external or internal scheduling signal, initiates the target value attribution and allocation logic and executes rule-based calculations based on preset parameters. This trigger instruction can originate from the task scheduling system, manual user operation, service event-driven, or periodic scheduled tasks, and can take the form of an HTTP interface request, a message queue event, a database trigger signal, etc. The trigger instruction typically includes additional parameters such as a time window, task identifier, processing scope, and priority level to define the specific context of target value processing.

[0061] Standardized transaction data is the dataset cleansed and converted based on the transaction data structure template in the previous step. Its field structure is standardized, semantics are consistent, and it is complete and accurate. Each record in standardized transaction data contains core fields related to target value attribution, such as service type, amount, time, and service recipient identifier. This serves as the foundational data source for subsequent attribution calculations and allocation logic.

[0062] Service hierarchy information refers to a multi-level organizational structure diagram constructed based on service type or service instance. This structure can include multiple granularity identifiers such as product lines, sub-services, interfaces, and functional modules, and establish parent-child dependency relationships. Service hierarchy information is used to determine the service attribution path corresponding to each standardized transaction data, ensuring accurate mapping to the correct service entity when calculating target values.

[0063] Target value determination parameters are a set of calculation rule parameters configured by the system based on business settings. These parameters include attribution weights, allocation factors, service importance levels, target value generation cycles, and threshold boundaries. They control how target values ​​are calculated from individual transaction data. Parameters can be fixed values, function expressions, or dynamically generated results, and can be adjusted dynamically by service type, time period, or transaction object.

[0064] Associated object hierarchy information refers to the organizational structure associated with the service beneficiary, including hierarchical relationships such as subsidiaries, business lines, cost centers, and functional departments. This information is used to map value from the service side to the beneficiary side when allocating target values. This hierarchy includes structures such as object identification, parent affiliation, allocation strategy, and responsibility definitions, enabling allocation path generation in a multi-beneficiary environment.

[0065] Carrying out target value attribution and target value allocation processing means combining all the above information to process each record in the standardized transaction data, clarifying which service level node and associated object node it belongs to, and calculating the target value amount generated by the record accordingly. Attribution processing includes service attribution path mapping and object attribution path identification; allocation processing includes the execution of target value allocation strategies, such as allocating value according to weight ratio, level priority, service duration, call frequency and other rules. The system binds the attribution path with the allocation result to generate the initial target value details. Each initial target value detail includes information such as the service attribution node, associated object node, attribution transaction data identifier, target value, source basis, etc., and marks its calculation success status and version number.

[0066] In one implementation, the system receives a target value processing trigger instruction through an interface, which contains the time window range and processing task type required for this processing. The system filters the data records within the time window from the standardized transaction data table, and establishes a mapping path based on the service type field and service level information to identify the service attribution node for each record. The system further calls the target value determination parameter corresponding to the attribution path, extracts the basic target value value from the transaction amount field, and adjusts the value according to parameters such as allocation weight and service level. Subsequently, the system matches the object identification field in the transaction data with the associated object level information, constructs an object attribution path, and splits the target value into multiple object nodes according to the object allocation rules. Each time a record is processed, an initial target value detail is generated, written into the intermediate result table, and attached with the identification of this processing task.

[0067] In another implementation, the system optimizes parallel processing of service attribution and object attribution. Standardized transaction data is divided into multiple processing batches, and service path mapping and object path identification are performed in parallel across multiple compute nodes. An in-memory computing structure stores service-level and object-level mappings, accelerating path finding. The target value assignment logic is executed by a configurable rule engine, supporting hot rule reloading and nested branch logic. Processing results are summarized in real time as initial target value details, with a rollback mechanism for exception interruptions.

[0068] Pre-simulation analysis can also be performed on attribution accuracy and allocation rationality. For example, before processing, simulation predictions can be made on target value results under different allocation parameters. After processing, statistical distribution analysis can be performed on the attribution level, object level, and value interval of the target value details for business review and anomaly identification.

[0069] Example description: In the field of financial technology business, the system extracts all payment records occurring within the monthly window from the standardized transaction data based on the received processing instructions, maps the payment channel field with the service level information, and clearly indicates that the payment belongs to the "External Payment Channel" sub-node under the "Third-Party Payment Service" module; the system identifies the target value unit price factor corresponding to the service level based on the configured target value determination parameters, and multiplies it by the transaction amount to generate the target value; the system further identifies the business line and branch to which the user account belongs through the user account information, and allocates the target value to the relevant institutions according to the weight set by the object level information, forming an initial target value detail.

[0070] In the medical and health business field, after the processing instruction is triggered, the system extracts medical records from the standardized transaction data, performs service attribution path matching on the department and service item in each record, and identifies that it belongs to the "clinical service-imaging diagnosis-CT" path; the system generates a target value based on the service unit price, number of medical treatments and time weight, and then allocates the target value to each responsible department based on the patient's medical insurance affiliation or hospital department structure and the object allocation rules, forming an initial target value detail for subsequent performance analysis or resource allocation.

[0071] This embodiment responds to target value processing trigger instructions and performs target value attribution and target value allocation based on standardized transaction data combined with service level information, target value determination parameters and associated object level information. It can achieve accurate attribution and reasonable allocation across service structures and multi-object systems, and ensure the accuracy, logical consistency and data traceability of target value calculation results in complex transaction contexts.

[0072] S40, detecting whether there is any data configuration missing or data logic conflict in the initial target value details, and if so, marking the initial target value details containing the data configuration missing or data logic conflict as abnormal target value details and outputting the abnormal target value details;

[0073] In this embodiment, detecting missing data configurations or data logic conflicts in the initial target value details involves performing integrity and consistency checks on each detail record after the initial target value details are generated to ensure that the data contained therein meets the formatting specifications, field completeness, and logical rationality required for subsequent processing. The initial target value details are generated from standardized transaction data in the previous processing flow. Each detail record should include multiple fields, such as the service attribution identifier, associated object identifier, target value value, attribution basis, and data version information.

[0074] Missing data configuration refers to situations where a key field value is missing or a configuration reference fails in the initial target value details. Common types include, but are not limited to: empty target value, unbound service tier, missing associated object identifier, zero allocation factor, unmatched rule number, unclosed attribution path, etc. The criteria for determining missing data configuration can be dynamically set based on the system configuration table or determined based on field mandatory status and rule mapping results.

[0075] Data logic conflicts refer to issues in the initial target value details that do not conform to the expected processing logic, such as logical contradictions between fields, value exceeding limits, inconsistent paths, and duplicate attribution. For example, the target value may be negative but the attribution type is positive service; the same transaction identifier may be mapped repeatedly to multiple service attribution nodes; there may be self-references or cross-level jumps in the object hierarchy path; or the attribution service node may not conform to the restriction conditions in the rule.

[0076] Marking and outputting abnormal target value details means that when the system identifies missing data configuration or data logic conflicts, the record is not included in the subsequent target value summary and recording process. Instead, it is labeled as abnormal and written to a separate abnormal output channel. This abnormal output can be a table of abnormal target values, an audit log table, or sent to a manual review queue. The marking operation typically includes updating the abnormal status field and recording the abnormality type, abnormality source field, processing time, task ID, and tracking number to facilitate subsequent review and remediation.

[0077] In one implementation, after the initial target value details are generated, the system traverses each record and checks whether its key fields are empty or invalid values. If a field is found to be empty, the data configuration is marked as missing according to the field type, and the processing status field of the detail is updated to "configuration exception". If the target value field is non-numeric or exceeds the set upper and lower limits, it is also marked as configuration missing. Subsequently, the system further checks whether there is a closed loop in the service attribution path and whether there is a path cross-level that does not conform to the rule definition. If an anomaly is found, it is judged as a logical conflict.

[0078] In another implementation, the system constructs a set of validation rules, defining distinct field validation logic for different service types, transaction sources, and object ownership dimensions. After the initial target value details are processed by the rules engine, records that fail validation are accompanied by an exception code, conflicting fields, and the basis for the determination, and written to a unified exception output table. This table also records the original transaction data identifier and the initial target value detail identifier, enabling rapid tracing back to the source data.

[0079] Machine learning models can also be introduced to identify potential logical conflicts. For example, a field distribution model of historical target value details can be constructed. When a certain dimension value combination deviates significantly from historical patterns, the system automatically identifies it as a possible logical anomaly and outputs it to the early warning module.

[0080] Example: In the fintech business area, when processing the initial target value details, the system discovered that some transaction data did not correctly match the service type, resulting in the target value field being empty, or repeated references to the same service node in the attribution path. The system immediately marked the detail as an exception and recorded the error fields as "Service Type" and "Path Structure", outputting them to the exception handling table for regular review by data operations personnel.

[0081] In the medical and health business field, if the system detects that there are cross-hospital jumps in the attribution paths of certain medical services in the initial target value details, or that the target value values ​​conflict with the service level combination (such as a high target value for first-level services), it will identify such details as logical conflicts, trigger an alarm mechanism, and output detailed results including the original medical record number, conflict path node, and target value field for review and rule adjustment.

[0082] This embodiment performs data configuration missing and logical conflict detection on the initial target value details, which can effectively eliminate abnormal results caused by data source errors, rule reference failures, or attribution calculation errors, prevent erroneous data from continuing to enter the downstream aggregation process, improve the stability and accuracy of the overall data processing chain, and provide high-quality input basis for manual review, automatic repair, and rule optimization.

[0083] S50, for the initial target value details that are not marked as abnormal target value details, aggregating and generating target value summary information according to the target value result presentation template;

[0084] In this embodiment, initial target value details that have not been marked as abnormal target value details are aggregated, meaning that after eliminating problematic records with missing data configurations or logical conflicts, the remaining data sets are structurally integrated. This integration process requires using the target value result presentation template as a benchmark, and through classification, aggregation, format adaptation, and other processes, generating target value summary information with a consistent structure and expression. Initial target value details that have not been marked as abnormal target value details refer to a set of detailed records that have passed the abnormality determination process and have been confirmed to meet the integrity and consistency requirements. Typically, fields such as service attribution, object path, allocated value, and transaction context information are retained.

[0085] The target value result presentation template is a formatting configuration resource used to guide the output structure of result aggregation. It typically consists of a set of field mapping rules, format expression specifications, and grouping and aggregation logic. The template defines target dimensions (such as service category, object hierarchy, and time period), aggregation methods (such as sum, average, and deduplication count), field order, and value formats (such as percentage, decimal places, and currency symbol). The existence of the target value result presentation template ensures that target value details from different sources and attribution dimensions have a unified output standard during the result generation process, facilitating subsequent display, storage, or synchronization.

[0086] Aggregating and generating target value summary information requires grouping the initial target value details according to the grouping logic specified in the target value result presentation template. Typically, this involves grouping the target value numeric fields using fields such as service identifier, object path, and statistical period as grouping keys. Aggregation logic is then applied to perform functions such as addition, maximum, minimum, or average calculations on the target value numeric fields, and outputting one or more summary result records. These summary records are then adjusted for field order and display format using field mapping rules, ultimately forming a unified structure for the target value summary information.

[0087] In one implementation, the system first reads the grouping dimensions and aggregation logic set in the target value result presentation template, groups the field values ​​in the initial target value details based on the template, and divides the groups by service level, object level, and processing cycle; performs a summation operation on the target value fields in each group to generate a total target value record for each dimension combination; then adjusts the field arrangement and formats the numerical precision according to the field order and format requirements set in the template to generate structured target value summary information.

[0088] In another implementation, the system loads the initial target value details into a temporary summary table structure and calls the embedded SQL aggregation logic or aggregation instruction set in the data analysis component to perform group aggregation operations on the specified fields. During execution, the system supports setting default fill strategies, unit conversion rules, and label addition logic for some fields to meet the output requirements of multiple scenarios. The output results are written to the target value summary information table after field alias replacement, order adjustment, and formatting. A version number and data source identifier are generated when necessary.

[0089] Target value result presentation templates can also be configured as a collection of multiple templates, with templates selected based on different business types or data sources. This dynamically drives aggregation logic to adapt to multiple service types and multi-object hierarchical scenarios. After aggregation, the system verifies the target value summary for field conflicts or aggregation failures, and outputs these to the log system.

[0090] Example: In the fintech business, different departments within a bank generate multiple service details based on the same transaction. After processing, these details are generated into initial target value details. After eliminating exceptions, the system uses templates to aggregate and summarize these details by service type, branch, and settlement period. Ultimately, a unified target value summary is generated for the finance department to perform monthly reconciliation and departmental cost aggregation.

[0091] In the medical and health business area, a hospital group conducts performance attribution and target assessment for the diagnosis and treatment projects of each branch. Based on the initial target value details, the system uses templates to aggregate data from each branch, department, and time period, and automatically generates target value summary information including the total target value corresponding to each service type and performance indicators, which facilitates unified evaluation and reporting by the management department.

[0092] This embodiment generates target value summary information by using a target value result presentation template to achieve structured integration of initial target value details from multiple sources, ensuring that the output results have a unified field structure, controllable value format and consistent expression, laying the foundation for subsequent data recording, visual display or cross-platform synchronization, and effectively improving the configurability and adaptability of the data processing process.

[0093] S60: Transmit the target value summary information to a preset target value recording target, and receive reception status feedback information of the target value recording target.

[0094] In this embodiment, transmitting the target value summary information to a preset target value recording destination means, after the target value summary information is structured and generated, accurately delivering the information to a designated receiving location in the system via a predefined communication mechanism or system interface. The target value summary information is the resultant data after structured aggregation processing, typically including target object identifiers, service dimensions, attribution periods, and target value fields. This information must be fully and accurately entered into the final recording system to ensure the accuracy of subsequent applications.

[0095] A preset target value recording target refers to a predefined data receiving and storage location within the system architecture. This may include a database table, distributed file storage node, external system interface, or cloud access address. This recording target serves not only as the final destination for data storage but also as the starting point for subsequent data services, report presentation, audit traceability, and other purposes. This recording target must have a communication path with the local system or current processing module and be able to respond to status feedback.

[0096] The transmission process may be implemented using a variety of protocols and methods, such as POST requests based on API calls, or data delivery through message queues, remote database writes, FTP push, etc. To ensure the reliability of data transmission, the system must perform structural verification and integrity checks on the target value summary information to prevent anomalies such as missing fields and format inconsistencies from affecting the result records.

[0097] Receipt status feedback refers to a set of status data or confirmation responses returned by the system mechanism after the target value record target receives and processes the target value summary information. This information typically includes information such as whether the reception was successful, the write status, error information, or processing time. By analyzing this reception status feedback information, the system can determine whether the current transmission is complete, whether a retry is required, or whether an exception notification is issued, ensuring closed-loop data flow management.

[0098] One implementation involves encapsulating the target value summary information in a JSON structure via HTTP and sending it to the configured receiving service address. This address corresponds to a logging service deployed within the internal microservices architecture. Upon receiving the request, the logging service parses the content and writes it to the target database table. Upon completion, the logging service returns a status response, including a success indicator, the number of data entries, and any errors. The original processing system then parses the data and updates the transmission log. Any abnormalities trigger a retransmission mechanism or an alarm.

[0099] Another implementation involves writing target value summaries to a designated topic in a Kafka message queue. The target value is recorded by an asynchronous consumer, which continuously listens to this topic and retrieves the data. After successfully processing and storing the data, the consumer sends a success flag to a response topic. The original system then listens to this response topic to obtain feedback, confirming and recording the status.

[0100] A database trigger mechanism can also be used. After the target value summary information is written into an intermediate table, the trigger will copy the data to the formal storage table of the target value record target. At the same time, the status confirmation function will be called through the trigger to return the processing completion flag field to the original processing module.

[0101] Example: In the healthcare sector, after a regional medical collaboration system integrates indicators across its branches, it pushes target value summaries to a provincial performance management platform via HTTP. Upon receiving the data, the platform generates a status response, which the original system then uses to record or retransmit, ensuring consistent target value records across multiple levels of management.

[0102] In the field of financial technology business, after multiple business units within a commercial bank group complete the collection of internal expense targets, they uniformly write the target value summary information into the receiving table of the headquarters' financial accounting platform. The receiving system notifies the original system of the processing status based on the feedback results of the warehousing status, thereby ensuring the integrity and compliance of the generated financial statements.

[0103] This embodiment forms a closed-loop data flow path by transmitting target value summary information to a preset target value recording destination and receiving feedback on the reception status. This ensures the reliability and traceability of the target value processing process. This mechanism not only improves the automation of data transmission but also enhances the ability to identify and handle anomalies, ensuring the accurate synchronization and recording of key information across multiple systems.

[0104] The present invention relates to the field of data processing technology and can be applied to business scenarios such as financial technology and medical health. It discloses an automated numerical value determination method, device, equipment and medium based on hierarchical information, including: establishing and storing service hierarchical information, target value determination parameters, transaction data structure templates, target value result presentation templates and associated object hierarchical information; receiving original transaction data and performing format conversion to generate standardized transaction data; generating initial target value details based on standardized transaction data and configuration information; identifying abnormal target value details and outputting them; generating target value summary information for initial target value details that are not marked as abnormal; transmitting target value summary information and receiving feedback information. The present invention realizes target value attribution and numerical value allocation across service units and associated objects through standardized transaction data structure and unified target value configuration system; ensures data quality through an abnormality identification mechanism; supports flexible aggregation and docking of results through aggregation templates, thereby improving the accuracy, adaptability and automation level of target value processing.

[0105] In one embodiment, the above step S10 includes:

[0106] S101, creating multiple independent service units as minimum metering entities, creating multiple aggregated service units as hierarchical integration entities, establishing subordinate associations between the independent service units and the aggregated service units, and assigning unique service identifiers to all service units to generate service hierarchical information;

[0107] S102, generating target value determination parameters by setting a basic target value value for the service unit, configuring a usage interval boundary threshold for the service unit, defining target value adjustment coefficients for different usage intervals, and binding the target value adjustment coefficients to the corresponding service unit via the unique service identifier;

[0108] S103, setting the required field set of the transaction data structure template to generate the transaction data structure template;

[0109] S104, setting layout constraints of the target value result presentation template to generate the target value result presentation template;

[0110] S105, by identifying a plurality of basic associated object units and a plurality of composite associated object units, establishing hierarchical links between the basic associated object units and the composite associated object units, and configuring target value distribution weights between the associated object units, thereby generating associated object hierarchical information;

[0111] S106, integrating the service level information, the target value determination parameters, the transaction data structure template, the target value result presentation template, and the associated object level information to form configuration information, and generating a configuration version identifier;

[0112] S107: Perform integrity check on the configuration information, and write the configuration information that passes the integrity check into a storage module.

[0113] In this embodiment, creating multiple independent service units as minimum measurement entities means that the system divides the service objects being measured or accounted for into the most basic, non-dividable service units. These independent service units typically represent actual, measurable resource items or service items, such as single computing resources, storage nodes, medical service items, and financial transaction sub-items in a cloud platform. They serve as the basic accounting granularity in the system, facilitating the subsequent allocation and aggregation of target values.

[0114] Creating multiple aggregated service units as hierarchical integration entities means building upon multiple independent service units to further consolidate the organizational or business structure. These aggregated service units can correspond to actual business departments, organizational units, service categories, and more, reflecting the subordination, ownership, and resource integration relationships between services.

[0115] Establishing subordinate relationships between independent service units and aggregated service units requires the system to construct a multi-level hierarchy, describing which aggregated service unit each independent service unit belongs to through parent-child mapping, pointer indexing, or path tree encoding, thereby forming a complete service hierarchy. Allocating unique service identifiers to all service units ensures consistency and identifiability throughout the entire data flow and target value processing process. This identifier should possess unique, immutable, and indexable properties.

[0116] Setting the basic target value of the service unit means setting an initial measurement value or performance expectation for each service unit, which serves as a basic reference for the target value attribution process. Configuring the usage interval boundary threshold of the service unit is to build a measurement mechanism based on segmented adjustment of usage intensity, so that the system can determine which interval it falls into based on the actual usage of the service. Defining the target value adjustment coefficient for different usage intervals is an important mechanism for making differentiated adjustments to the original target value. It may be modeled using linear, piecewise linear or nonlinear functions, and adjusting its target value after mapping the boundary value to the corresponding interval. Common examples include the lower the compensation coefficient for the higher the frequency of diagnosis and treatment, and the higher the incentive multiple for the larger the transaction volume. Binding the target value adjustment coefficient to the corresponding service unit through a unique service identifier is to ensure that the adjustment rules and parameters of the service unit can be accurately found when executing the target value allocation process through an association mapping mechanism.

[0117] The mandatory fields in the transaction data structure template are set to unify the input data structure and ensure its standardization. Mandatory fields typically include key fields such as the transaction ID, service identifier, usage quantity, occurrence time, and service status. The system constructs a standardized data model by defining field names, field types, and field formats, forming a transaction data structure template for subsequent data cleaning and analysis.

[0118] Setting the layout constraints of the target value result presentation template refers to the structured definition of the target value summary or result report format for the final display, including the data table field order, grouping method, display unit, field label, etc., which is used to unify the visual display format and interface transmission standard of the target value output.

[0119] Identifying multiple basic association object units and multiple composite association object units means breaking down the objects to which the target values ​​are ultimately attributed into different granularities, such as individuals, departments, projects, branches, and so on. The basic association object unit is the lowest-level unit, and the composite association object unit is an aggregate object composed of multiple basic units. The purpose of establishing a hierarchical link between the two is to support a multi-level, multi-role target value attribution structure, and to achieve automatic delivery and decomposition of target values ​​by level. Configuring the target value allocation weights between association object units is to quantitatively control the target value allocation results based on the business volume, responsibility boundaries, or degree of participation of different objects.

[0120] Integrating service-level information, target value determination parameters, transaction data structure templates, target value result presentation templates, and associated object-level information to form configuration information means uniformly encapsulating all of the above parameter data used to drive the target value processing process to generate a structured configuration data object for parameter driving of subsequent processing modules.

[0121] Generating a configuration version identifier refers to generating a unique identifier for the current configuration information to support version management, traceability comparison, and dynamic switching. Performing integrity checks on the configuration information typically includes field validation, association consistency checks, and null value detection to ensure that all configuration items are valid for execution. After the integrity check passes, the configuration information is written to the storage module to ensure persistent storage and subsequent callability of the configuration.

[0122] This embodiment implements a highly adaptable target value processing flow driven by configuration by building a unified service hierarchy system, target value configuration mechanism, data structure template, and object attribution model. This approach not only offers high flexibility and scalability, but also ensures data consistency and processing accuracy. It is particularly suitable for metering attribution tasks in multi-level, multi-role, and multi-scenario environments, effectively resolving issues such as the lack of unified configuration, poor rule adaptability, and fragmented processing links in existing systems.

[0123] In one embodiment, the above step S20 includes:

[0124] S201, obtaining raw transaction data to be processed through a data receiving interface, and identifying a field set in the raw transaction data;

[0125] S202, based on the mandatory field set defined in the transaction data structure template, verify whether the field set contains all mandatory fields;

[0126] S203: If it is detected that a required field is missing, the missing field content is added to the field set according to a preset field filling strategy;

[0127] S204: If field redundancy is detected, then based on the field filtering strategy of the transaction data structure template, redundant fields that do not belong to the required field set are removed from the field set;

[0128] S205, performing field order sorting and field type conversion on the field set according to the field order and field type strategy in the transaction data structure template;

[0129] S206: Generate standardized transaction data based on the converted field set.

[0130] In this embodiment, acquiring raw transaction data to be processed through the data reception interface involves the system interacting with external data sources through a pre-defined data collection module to collect transaction-related data records from log files, business middleware, heterogeneous databases, file storage directories, or message middleware. This interface supports multiple protocol access methods, including RESTful API, FTP, SFTP, direct database connection, and middleware subscription. It features asynchronous or synchronous reception mechanisms to ensure stable data access in high-concurrency business scenarios.

[0131] Identifying field sets in raw transaction data involves using a parsing module to extract the field list from the raw data based on its format (e.g., JSON, XML, CSV, table structure, key-value pair structure, etc.). This field set typically includes information such as field name, field value, and field type, facilitating subsequent field matching and template verification.

[0132] Verifying that all required fields are included in the set of mandatory fields defined in the transaction data structure template involves comparing the parsed field set against a pre-defined transaction data structure template. This template, automatically generated by the system administrator or through configuration rules, specifies the key fields that must be present in each transaction data entry for target value attribution. These fields may include transaction ID, transaction time, service ID, quantity, source system, and attribution institution, ensuring data processability and consistency.

[0133] If a required field is detected as missing, the system uses a pre-defined field filling strategy to populate the missing field content within the field set. This refers to the system's support for automatic field completion, which uses field context derivation, default value rules, historical cached data, or rule tables to fill in missing fields. For example, if the "Department" field is missing, the system can deduce and fill it based on the transaction source IP or device number; if the transaction time is missing, a default value can be added based on the receipt time.

[0134] If field redundancy is detected, the transaction data structure template's field filtering strategy will remove redundant fields that are not part of the required field set from the field set. This means the system performs field filtering to remove fields not explicitly required by the template, reducing processing costs and interference caused by invalid fields. The filtering strategy can be implemented in a field whitelist mode or conditionally based on field usage or field weight.

[0135] Based on the field order and field type policies in the transaction data structure template, the system performs field ordering and field type conversion on the field set. This means that the system standardizes the field set to a standardized format, including field order and field data type. Field ordering ensures that subsequent processing modules do not misalign their structured data reading. Field type conversion includes operations such as converting strings to date format, converting floating-point numbers to integers, and formatting Boolean fields. This operation relies on a field type mapping table and a standardized conversion engine to perform field-by-field parsing and reconstruction.

[0136] Generating standardized transaction data based on the converted field set means that after the system completes field content, eliminates redundancy, organizes the sequence, and standardizes the types, it encapsulates the field set into a standardized data object according to the format specifications of the transaction data structure template. This data object is typically a structured record with field integrity, consistency, and processability, serving as the direct input source for subsequent target value attribution, allocation, and verification.

[0137] This embodiment automatically converts raw transaction data into structured, standardized data through field extraction, template matching, data verification, and formatting, improving both the standardization of data processing and the system's fault tolerance. This approach supports high-precision formatting and normalization in situations involving multi-source data input, inconsistent field naming, and complex data formats, significantly improving the accuracy and stability of subsequent data processing chains. It effectively addresses existing data integration issues such as the difficulty in unifying field differences, the inability to complete missing data, and non-standard field types.

[0138] In one embodiment, the above step S30 includes:

[0139] S301, parsing the target value processing trigger instruction to obtain target value processing parameters, wherein the target value processing parameters include a processing time range and a business type identifier;

[0140] S302, determining a target service unit corresponding to the standardized transaction data based on the independent service unit and the aggregated service unit in the service level information and according to the business type identifier;

[0141] S303, determining a target associated object unit corresponding to the standardized transaction data based on the basic associated object unit and the composite associated object unit in the associated object hierarchy information and according to the processing time range;

[0142] S304: Determine a target value for the standardized transaction data based on the basic target value, the usage interval boundary threshold, and the target value adjustment coefficient in the target value determination parameters;

[0143] S305: Combine the target service unit, the target-related object unit, and the target value to generate an initial target value detail.

[0144] In this embodiment, upon receiving a target value processing trigger instruction, the system first performs semantic parsing on the instruction, extracting processing parameters, including the time range and business type identifier. The processing time range is used to limit the time window of transaction data covered by this attribution processing. For example, it can be set to the current day, the current month, or a custom time period to control the scope of data screening. The business type identifier indicates the business scope of this processing operation, such as information technology services, customer service support, or product delivery, to guide the subsequent attribution matching of service units and object units.

[0145] After completing parameter parsing, the system matches service attribution based on service-level information. Service-level information includes configured independent service units and aggregated service units. The system selects service units that match the business type in the service level based on the business type identifier. This process not only includes the comparison of business tags, but can also be extended to use regular expressions, classification tag mapping tables, or upper and lower logical matching mechanisms based on knowledge graphs to improve attribution accuracy. The matched target service unit may be a single service unit or all subnodes under a certain aggregate unit, depending on the configured parsing depth and business identifier granularity.

[0146] Next, the system matches the hierarchical information of associated objects by processing the time range, filtering out the object entities corresponding to the transaction data from multiple basic and composite associated object units. This matching process may rely on the organizational structure tree, departmental responsibility attribution rules, historical attribution records, and dynamic time dimension configuration. Basic associated objects typically represent the smallest granularity of responsible entities, such as employees, departments, and legal entities; composite associated objects represent aggregate structures, such as business groups, management lines, or parent-subsidiary companies. The system automatically selects the object attribution mapping relationship that is valid at the corresponding time point based on the comparison of the transaction time with the preset associated object validity period.

[0147] Subsequently, the system performs numerical calculations on the standardized transaction data based on the target value determination parameters. The basic target value is the initial measurement value set for different service units or transaction types, usually the target billing benchmark value corresponding to each transaction. The usage interval boundary threshold is used to divide the service-related usage in the transaction into different segments, and the target value adjustment coefficient is the weighting or scaling coefficient defined for different intervals. The system can determine which interval it falls into based on the usage field in the transaction record (such as CPU hours, number of calls, processing amount, etc.), and select the corresponding adjustment coefficient to correct the basic target value to obtain the final target value. This calculation process supports nonlinear mapping, interval interpolation or table-driven rules to achieve highly adaptive target value calculation logic.

[0148] Finally, the system combines the target service unit, target-related object unit, and target value into a structured record, forming an initial target value breakdown. This record structure must preserve the original transaction data's unique identifier, attribution path, value source, and calculation path to support subsequent data verification, anomaly identification, and result presentation. The breakdown generation process can be integrated with multi-threaded concurrent processing, transaction control, and status marking mechanisms to ensure data integrity and attribution stability in high-frequency trading environments.

[0149] This embodiment implements a multi-level target value generation process across service and object dimensions by parsing trigger instructions and performing attribution matching and numerical calculations on standardized transaction data based on preset configuration information. This approach has flexible business adaptation capabilities and can cope with the needs of scenarios with dynamic changes in organizational structure, diversified service types, and complex measurement logic. In particular, when dealing with heterogeneous business data and cross-unit responsibility attribution issues, it can effectively solve the problems existing in the existing system, such as inaccurate service and object matching, rigid target value generation logic, and lack of dynamic configuration support, thereby greatly improving the intelligence of attribution processing and the system's scalability.

[0150] In one embodiment, the above step S40 includes:

[0151] S401, based on the service registration list, detecting whether the target service unit included in each record in the initial target value details has been registered;

[0152] S402, based on the object registration list, detecting whether the target-associated object unit contained in each record in the initial target value details has been registered;

[0153] S403, detecting whether the target value of each record in the initial target value details is within a valid range according to the verification policy threshold;

[0154] S404, when it is detected that the target service unit is not registered in the service registration list, the target associated object unit is not registered in the object registration list, or the target value is not within the valid range defined by the verification policy threshold, the initial target value details of the corresponding record are marked as abnormal target value details;

[0155] S405, assigning an abnormality classification code indicating a specific abnormality cause to the abnormal target value details;

[0156] S406: Push the abnormal target value details including the abnormal classification code to the abnormal data review interface.

[0157] In this embodiment, after the system generates the initial target value details, to ensure the accuracy and traceability of subsequent processing results, it needs to perform validity testing and logical consistency verification on these details. This verification process involves three core dimensions: service unit registration status, associated object registration status, and value legitimacy verification.

[0158] First, the system verifies the validity of the target service unit based on the service registration list. The service registration list is a structured service directory containing the identifiers of all configured and active service units. The system compares the target service unit identifier contained in each record in the initial target value details to confirm whether the identifier exists in the registration list. If a service unit is found to be unregistered or has been deregistered, the record in that record has missing data configuration.

[0159] Next, the system verifies the validity of the target-associated object unit by referencing the object registration list. This list includes all valid identifiers for basic and composite associated objects, along with their organizational affiliation information, effective time periods, and status flags indicating their eligibility for target allocation. By matching the object identifiers in the initial target value details with the object registration list, the system determines whether the object is legally owned and valid, thereby identifying unregistered or invalid object units.

[0160] After completing the dual registration verification for the service and object, the system also performs a validity check on the target value. This check is based on the thresholds of the verification policy, which includes target value range boundaries configured for different service types, business types, or associated objects. These boundaries can be static values ​​or dynamically generated upper and lower limits based on historical distributions. The system compares the target value in each initial target value detail with the corresponding threshold range. If it exceeds the defined range, it is marked as a value logic conflict.

[0161] If any of the three types of checks fail, the system marks the corresponding details as abnormal target value details and further assigns an abnormality classification code. The abnormality classification code accurately expresses the type and source of the abnormality, such as unregistered service, invalid object, or target value out of bounds. This classification mechanism supports subsequent rapid location and grouping, and can be combined with the coding structure to reflect the abnormality level, impact scope, and remediation suggestions.

[0162] After anomaly annotation is complete, the system pushes details, including the anomaly classification code, to the anomaly data review interface. This interface is typically a set of standardized APIs or data queues, used to connect with audit modules, manual verification systems, or automated remediation engines. The push process supports concurrent channels, multi-class message priority scheduling, and result receipt status logging to ensure the timeliness and integrity of anomaly data processing.

[0163] This embodiment performs multi-dimensional verification of the initial target value details based on the registration list and verification strategy, and can immediately identify abnormal problems at the service, object, and value levels after the attribution processing is completed. This method improves the system's response speed and classification processing capabilities to abnormal data, preventing erroneous target value information from entering subsequent links and avoiding impacts on cost settlement, performance evaluation, or resource allocation. At the same time, a structured exception tracking capability is established through the exception classification code mechanism, which facilitates the system to automatically locate the source of the problem, achieve continuous optimization and closed-loop improvement, and thus enhance the stability and reliability of the entire system.

[0164] In one embodiment, the above step S50 includes:

[0165] S501, filtering the records in the initial target value details that are not marked as abnormal target value details to obtain a valid target value detail record set;

[0166] S502, grouping the effective target value detail record set according to the grouping dimension defined in the target value result presentation template;

[0167] S503, applying the summary strategy defined in the target value result presentation template to each group of record sets after the grouping process to determine the summary value of the target numerical field;

[0168] S504 , filling the grouping dimension and the summary value into a designated position in the target value result presentation template according to the layout constraints in the target value result presentation template, and generating target value summary information including the filled content.

[0169] In this embodiment, after identifying abnormal data, the system needs to summarize and organize the remaining initial target value details that are logically consistent and complete to form a structured summary output. This process is implemented based on a preset target value result presentation template, which has defined the grouping dimensions, aggregation strategy, and layout constraints during the configuration phase.

[0170] First, the system filters the data records in the initial target value details that are not marked as abnormal to form a valid target value detail record set. This filtering operation is based on the abnormal identification field and excludes records containing abnormal classification codes. The valid record set must fully include the target service unit, target related object unit, and target value field, and have been verified for compliance through the aforementioned verification process.

[0171] Next, the system groups the valid record sets according to the grouping dimensions set in the target value result presentation template. Grouping dimensions may include, but are not limited to, service unit identifiers, object level identifiers, business types, time periods, etc. Each dimension, as a division granularity, affects the accuracy of the summary granularity and the integrity of the dimension intersection. During the grouping process, the system must simultaneously support multi-dimensional combinations, null value elimination, and default grouping strategies to ensure that data is fully covered and no groups are missed.

[0172] After grouping, the system applies the aggregation strategy defined in the template to each set of records, calculating the aggregate value of the target numeric field. Aggregation strategies typically include sum, average, maximum, minimum, or weighted aggregation. Weighted aggregation combines the target values ​​in the associated object hierarchy information with weights assigned to them, ensuring that the results reflect the contributions of different objects in the hierarchy. The system automatically performs data type standardization and unit conversion during the aggregation process to ensure consistent and readable results.

[0173] The system then completes the structured output of the summary results based on the layout constraints defined in the template. Layout constraints specify the position, format, and display logic of the grouping dimension fields and summary fields in the output structure, such as the mapping rules for rows and columns in a table structure or the coordinate mapping of dimensions and metrics in a chart structure. The system populates the grouping dimensions and corresponding summary values ​​into the specified locations of the target value result presentation template, generating target value summary information that can be read, transmitted, or displayed. This summary information can be organized and stored in the form of tables, JSON structures, CSV files, etc., with cross-platform compatibility and visual rendering adaptability.

[0174] This embodiment performs structured aggregation on initial target value details that are not marked as abnormal, quickly generating target value summaries that reflect benefit relationships, resource usage, and cost allocation. Combined with templated grouping dimensions and aggregation strategy configuration, this approach not only improves the consistency and standardization of output data, but also enhances the ability to flexibly present different business perspectives (such as service dimensions, object dimensions, or time dimensions), helping various management departments quickly obtain key indicators, analyze cost structures, and optimize resource allocation paths.

[0175] In one embodiment, the above step S60 includes:

[0176] S601, formatting and encapsulating the target value summary information to generate a target value transmission message;

[0177] S602, connecting to the preset interface address of the target value recording target through a secure transmission protocol;

[0178] S603, sending the target value transmission message to the target value recording target;

[0179] S604, receiving reception status feedback information returned by the target value record target;

[0180] S605: Update the transmission status log according to the reception status feedback information.

[0181] In this embodiment, after the aggregation and structured output of the target value summary information is completed, the information needs to be transmitted to the designated recording system to realize data archiving, subsequent query or inter-system linkage processing. In order to ensure the standardization of data structure and the security and stability of the communication process during the transmission process, the system needs to format and encapsulate the target value summary information first. The encapsulation process will reorganize the grouping dimension fields and the summary value fields into a standardized transmission structure according to the data interface specifications required by the target value record target. The encapsulation method may include constructing a nested JSON structure, an XML structure, a fixed-length field message with an identifier, or a customized key-value pair collection structure, depending on the parsing capability of the target system and the transmission protocol specifications.

[0182] After encapsulation, the system connects to the target value through a preset secure transmission protocol and records the target's interface address. This interface address may be a service endpoint address within the local area network (LAN) or an external system address accessed through a VPN. Secure transmission protocols should have encrypted channels, authentication mechanisms, and message integrity verification mechanisms. Common forms include HTTPS, SFTP, WebSocket over TLS, and gRPC over TLS. During the connection establishment process, the system must load a trust certificate, transmission token, or interface key to verify the identities of both parties and ensure the tamper-proof nature of the transmitted content.

[0183] After the connection is established, the system sends the target value transmission message to the target value recording target. This process may use a synchronous call method to wait for the target system's response, or it may use an asynchronous message queue or event-triggered model for delivery. The system supports configuration of control parameters such as timeout, retry count, and failure forwarding strategy to improve fault tolerance.

[0184] After receiving a message, the target system returns a status message, typically including the success or failure of the reception, an error code, a reception timestamp, and a system processing identifier. The system parses this feedback and uses the results to update the transmission status log. This log includes fields such as transmission time, message identifier, target system address, transmission result, response delay, and error details, making it easier for operations and maintenance personnel to track transmission paths, troubleshoot anomalies, and re-deliver data.

[0185] Example: Within a large financial holding group, multiple subsidiaries share a unified customer relationship management (CRM) platform. Operating costs for system maintenance, data storage, and customer service need to be shared across the various subsidiaries. To achieve a clear, transparent, and auditable cost-sharing mechanism, the group deployed this system to perform target value attribution and allocation.

[0186] First, service hierarchy information was established in the group management system, including independent service units with various platform modules (such as customer data query, customer service response, data analysis, etc.) as the smallest unit, as well as aggregated service units such as data services and operation services formed by functional aggregation, and a unique service identifier was configured for each service unit. Next, target value determination parameters were set, and basic allocation amounts, usage ranges, and adjustment coefficients were set for different service units to reflect the intensity of system resource usage by different subsidiaries. The transaction data structure template was further configured, including fields such as subsidiary number, service module name, call time, and number of operations; the result presentation template was set, requiring summary display according to subsidiary and service type, and the display layout of the allocation results was configured. Finally, the associated object hierarchy information was constructed, and the business lines or departments under each subsidiary were constructed as basic or composite associated objects, and corresponding allocation weights were configured for them.

[0187] The system then receives usage log data from the CRM platform as raw transaction data, automatically identifying and validating fields, formatting and converting it according to the transaction data template, completing missing fields and removing invalid fields to generate standardized transaction data. Upon receiving a trigger for quarterly settlement, the system analyzes the processing timeframe and the business types involved. It matches the corresponding service units based on service level information and the service module fields in the transaction data, and determines associated objects based on the subsidiary's usage scenarios. Based on the established baseline target value and usage interval adjustment coefficient, the system calculates the target value for each transaction and combines it into a detailed initial target value.

[0188] After generating the initial target value details, the system immediately performs a validity check on it, verifying whether the service ID and object ID are in the registration list one by one, and whether the target value is within a reasonable range. If there are abnormal situations such as unregistered service units or exceeding the fee cap, an abnormal classification code will be assigned and the data will be pushed to the review interface for manual review.

[0189] For target value details that pass verification, the system groups them by subsidiary number and service type according to the template's aggregation logic. It then summarizes each subsidiary's target values ​​for each service category, creating a structured allocation summary. The system then creates a layout based on the target value presentation template, generating a final target value summary and encapsulating it into a standardized transmission message, which is then sent to the group's financial settlement system via a secure connection. Upon receiving status feedback from the target system, the system logs the successful transmission.

[0190] This process ultimately resulted in clear, reasonable, and auditable cost allocation results for each subsidiary, and established a closed loop of exception tracking and logging for the entire process.

[0191] Similarly, in a large general hospital, multiple clinical departments share medical consumables such as disposable surgical kits and anesthesia materials from the central supply room. The procurement costs of these consumables must be allocated among relevant departments, functional departments, and the supply center based on actual usage and accountability, ensuring internal cost transparency and controllable usage.

[0192] First, the hospital management platform established service hierarchy information, coded each type of consumables as an independent service unit, and composed multiple aggregated service units based on procurement type, using department, etc., which were uniformly coded as service identifiers. The target value determination parameters define the reference price, procurement quantity segmentation threshold, and price adjustment coefficient for each type of consumables, which are used for subsequent numerical calculations. The transaction data structure template is set to include fields such as patient ID, consumable number, using department, surgery number, and usage time. The target value result presentation template requires the generation of a two-dimensional matrix by consumable category and department to display the summary costs. In the associated object hierarchy, clinical departments, anesthesia departments, operating rooms, etc. are used as basic objects, and a composite object structure is formed by binding the surgical team, and responsibility weights are set.

[0193] After the system receives daily consumables collection data from the central supply room, it extracts and verifies the data fields, supplements missing surgery number fields, removes historical format fields, unifies field order and data types, and generates standardized transaction data.

[0194] After receiving the settlement trigger instruction at the end of each month, the system automatically parses the date range of the month and the relevant consumables type identification, identifies the service unit and associated objects to which each record in the standardized transaction data belongs, calculates the attributable cost of each record according to the usage and price rules, and outputs the initial target value details.

[0195] The system automatically verifies for each record whether the corresponding consumables have been filed, registered by the responsible department, and whether the usage value is reasonable. If a non-registered department uses the consumables or the price exceeds the limit, the record will be marked as an abnormal target value and a detailed explanation of the exception will be attached, such as "Unauthorized Department Use." The remaining valid records will be collected and summarized, and the expenses will be consolidated according to the grouping dimensions set by the template (such as consumable category and responsible department) to generate a two-dimensional summary table.

[0196] The final summary results are formatted and encapsulated into a structured message and transmitted to the interface server of the financial settlement center via the hospital's local area network. After the financial system returns the "record successful" status, the system will store the relevant log records in the database to form a complete tracking record.

[0197] This process clarifies the responsibility for consumables, standardizes process data, and provides structured early warnings for abnormal usage behaviors, ensuring the consistency and efficiency of hospital resource allocation and cost accounting.

[0198] This embodiment encapsulates formatted target value summary information and sends it to a pre-set recording destination using a secure protocol. This not only ensures the secure exchange of target value result data between different systems, but also enhances the automated transfer and closed-loop processing capabilities of data flows between systems. The feedback information reception and log update mechanism ensures that every data transmission is fully recorded and traceable, facilitating subsequent anomaly investigation, compliance audits, and data quality tracking, thereby enhancing system reliability, transparency, and data processing efficiency.

[0199] In one embodiment, an automatic value determination device based on hierarchical information is provided, and the automatic value determination device based on hierarchical information corresponds one-to-one to the automatic value determination method based on hierarchical information in the above embodiment. Figure 3 , Figure 3 This is a functional module diagram of a preferred embodiment of the hierarchical information-based automated numerical determination device of the present invention. It includes a configuration management module 10, a data access module 20, a target value calculation module 30, an anomaly identification module 40, a result aggregation module 50, and a result push module 60. Each functional module is described in detail below:

[0200] Configuration management module 10, for establishing and storing service hierarchy information, target value determination parameters, transaction data structure templates, target value result presentation templates, and associated object hierarchy information;

[0201] The data access module 20 is used to receive the original transaction data to be processed and format the original transaction data according to the transaction data structure template to generate standardized transaction data;

[0202] The target value calculation module 30 is configured to respond to the target value processing trigger instruction, perform target value attribution and target value allocation processing based on the standardized transaction data and in combination with the service level information, the target value determination parameters, and the associated object level information, and generate an initial target value detail;

[0203] An abnormality identification module 40 is used to detect whether there is any data configuration missing or data logic conflict in the initial target value details. If so, the initial target value details containing the data configuration missing or data logic conflict are marked as abnormal target value details and output;

[0204] A result summary module 50 is configured to aggregate and generate target value summary information for initial target value details that are not marked as abnormal target value details according to the target value result presentation template;

[0205] The result push module 60 is configured to transmit the target value summary information to a preset target value recording target and receive reception status feedback information of the target value recording target.

[0206] In one embodiment, the configuration management module 10 is specifically configured to:

[0207] By setting the basic target value value of the service unit, configuring the usage interval boundary threshold of the service unit, defining the target value adjustment coefficient of different usage intervals, and binding the target value adjustment coefficient to the corresponding service unit through the unique service identifier, the target value determination parameter is generated;

[0208] Set the required fields of the transaction data structure template and generate the transaction data structure template;

[0209] Setting the layout constraints of the target value result presentation template and generating the target value result presentation template;

[0210] By identifying a plurality of basic associated object units and a plurality of composite associated object units, establishing hierarchical links between the basic associated object units and the composite associated object units, and configuring target value distribution weights between the associated object units, associated object hierarchical information is generated;

[0211] Integrating the service level information, the target value determination parameters, the transaction data structure template, the target value result presentation template, and the associated object level information to form configuration information, and generating a configuration version identifier;

[0212] An integrity check is performed on the configuration information, and the configuration information that passes the integrity check is written into the storage module.

[0213] In one embodiment, the data access module 20 is specifically configured to:

[0214] Obtaining raw transaction data to be processed through a data receiving interface, and identifying a set of fields in the raw transaction data;

[0215] Based on the mandatory field set defined in the transaction data structure template, verifying whether the field set contains all mandatory fields;

[0216] If it is detected that a required field is missing, the missing field content is added to the field set according to the preset field filling strategy;

[0217] If field redundancy is detected, then based on the field filtering strategy of the transaction data structure template, redundant fields that do not belong to the required field set are removed from the field set;

[0218] Performing field order sorting and field type conversion on the field set according to the field order and field type strategy in the transaction data structure template;

[0219] Generate standardized transaction data based on the transformed field set.

[0220] In one embodiment, the target value calculation module 30 is specifically configured to:

[0221] Parsing the target value processing trigger instruction to obtain target value processing parameters, wherein the target value processing parameters include a processing time range and a business type identifier;

[0222] Determining a target service unit corresponding to the standardized transaction data based on the independent service unit and the aggregated service unit in the service level information and according to the business type identifier;

[0223] Determining a target associated object unit corresponding to the standardized transaction data based on the basic associated object unit and the composite associated object unit in the associated object hierarchy information and according to the processing time range;

[0224] Determine the target value of the standardized transaction data according to the basic target value value, the usage interval boundary threshold and the target value adjustment coefficient in the target value determination parameters;

[0225] The target service unit, the target-related object unit, and the target value are combined to generate an initial target value specification.

[0226] In one embodiment, the anomaly identification module 40 is specifically configured to:

[0227] According to the service registration list, detecting whether the target service unit included in each record in the initial target value details has been registered;

[0228] According to the object registration list, detecting whether the target-related object unit contained in each record in the initial target value details has been registered;

[0229] According to the verification strategy threshold, detecting whether the target value of each record in the initial target value details is within the valid range;

[0230] When it is detected that the target service unit is not registered in the service registration list, the target associated object unit is not registered in the object registration list, or the target value is not within the valid range defined by the verification policy threshold, the initial target value details of the corresponding record are marked as abnormal target value details;

[0231] Assigning an abnormality classification code indicating a specific abnormality cause to the abnormal target value details;

[0232] The abnormal target value details including the abnormal classification code are pushed to the abnormal data review interface.

[0233] In one embodiment, the result aggregation module 50 is specifically configured to:

[0234] Filtering the records in the initial target value details that are not marked as abnormal target value details to obtain a valid target value detail record set;

[0235] Grouping the effective target value detail record set according to the grouping dimension defined in the target value result presentation template;

[0236] Applying the summary strategy defined in the target value result presentation template to each set of records after the grouping process to determine the summary value of the target numerical field;

[0237] According to the layout constraints in the target value result presentation template, the grouping dimension and the summary value are filled into the designated position in the target value result presentation template to generate target value summary information including the filled content.

[0238] In one embodiment, the result push module 60 is specifically configured to:

[0239] Formatting and encapsulating the target value summary information to generate a target value transmission message;

[0240] Connecting to a preset interface address of the target value recording target through a secure transmission protocol;

[0241] Sending the target value transmission message to the target value recording target;

[0242] Receive reception status feedback information returned by the target value record target;

[0243] The transmission status log is updated according to the reception status feedback information.

[0244] In one embodiment, a determination device is provided. The determination device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The determination machine device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the determination machine device is used to provide determination and control capabilities. The memory of the determination machine device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a determination machine program and a database. The internal memory provides an environment for the operation of the operating system and the determination machine program in the non-volatile storage medium. The network interface of the determination machine device is used to communicate with an external user terminal through a network connection. When the determination machine program is executed by the processor, it realizes the functions or steps on the service side of an automated numerical determination method based on hierarchical information.

[0245] In one embodiment, a determination device is provided. The determination device may be a user terminal, and its internal structure diagram may be as follows: Figure 5 As shown. The determination machine device includes a processor, memory, network interface, display screen and input device connected via a system bus. Among them, the processor of the determination machine device is used to provide determination and control capabilities. The memory of the determination machine device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a determination machine program. The internal memory provides an environment for the operation of the operating system and the determination machine program in the non-volatile storage medium. The network interface of the determination machine device is used to communicate with an external server through a network connection. When the determination machine program is executed by the processor, it realizes the functions or steps on the user side of an automated numerical determination method based on hierarchical information.

[0246] In one embodiment, a determination machine device is provided, including a memory, a processor, and a determination machine program stored in the memory and executable on the processor. When the processor executes the determination machine program, the following steps are implemented:

[0247] Establish and store service hierarchy information, target value determination parameters, transaction data structure templates, target value result presentation templates, and associated object hierarchy information;

[0248] Receiving raw transaction data to be processed, and formatting and converting the raw transaction data according to the transaction data structure template to generate standardized transaction data;

[0249] In response to the target value processing trigger instruction, based on the standardized transaction data and in combination with the service level information, the target value determination parameters and the associated object level information, target value attribution and target value allocation processing are performed to generate initial target value details;

[0250] Detect whether there is any data configuration missing or data logic conflict in the initial target value details. If so, mark the initial target value details containing the data configuration missing or data logic conflict as abnormal target value details and output them;

[0251] For the initial target value details that are not marked as abnormal target value details, generate target value summary information according to the target value result presentation template;

[0252] The target value summary information is transmitted to a preset target value recording target, and reception status feedback information of the target value recording target is received.

[0253] In one embodiment, a determination machine readable storage medium is provided, on which a determination machine program is stored. When the determination machine program is executed by a processor, the following steps are implemented:

[0254] Establish and store service hierarchy information, target value determination parameters, transaction data structure templates, target value result presentation templates, and associated object hierarchy information;

[0255] Receiving raw transaction data to be processed, and formatting and converting the raw transaction data according to the transaction data structure template to generate standardized transaction data;

[0256] In response to the target value processing trigger instruction, based on the standardized transaction data and in combination with the service level information, the target value determination parameters and the associated object level information, target value attribution and target value allocation processing are performed to generate initial target value details;

[0257] Detect whether there is any data configuration missing or data logic conflict in the initial target value details. If so, mark the initial target value details containing the data configuration missing or data logic conflict as abnormal target value details and output them;

[0258] For the initial target value details that are not marked as abnormal target value details, generate target value summary information according to the target value result presentation template;

[0259] The target value summary information is transmitted to a preset target value recording target, and reception status feedback information of the target value recording target is received.

[0260] It should be noted that the above functions or steps that can be implemented by the machine-readable storage medium or the machine device can be referred to the relevant descriptions on the server side and the user side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0261] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a determination machine program, and the determination machine program can be stored in a non-volatile determination machine-readable storage medium. When the determination machine program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0262] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0263] It should be noted that if any software tools or components other than those of the Company appear in the embodiments of this application, they are merely for illustration and do not represent actual use. The above embodiments are intended only to illustrate the technical solutions of the present invention, not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein with equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. An automated numerical value determination method based on hierarchical information, characterized in that: The following steps are involved: Establish and store service hierarchy information, target value determination parameters, transaction data structure templates, target value result presentation templates, and associated object hierarchy information; Receiving raw transaction data to be processed, and formatting and converting the raw transaction data according to the transaction data structure template to generate standardized transaction data; In response to the target value processing trigger instruction, based on the standardized transaction data and in combination with the service level information, the target value determination parameters and the associated object level information, target value attribution and target value allocation processing are performed to generate initial target value details; Detect whether there is any data configuration missing or data logic conflict in the initial target value details. If so, mark the initial target value details containing the data configuration missing or data logic conflict as abnormal target value details and output them; For the initial target value details that are not marked as abnormal target value details, generate target value summary information according to the target value result presentation template; The target value summary information is transmitted to a preset target value recording target, and reception status feedback information of the target value recording target is received.

2. The method for automatically determining a numerical value based on hierarchical information according to claim 1, wherein: Establish and store service hierarchy information, target value determination parameters, transaction data structure templates, target value result presentation templates, and associated object hierarchy information, including: Generate service hierarchy information by creating multiple independent service units as minimum metering entities, creating multiple aggregate service units as hierarchical integration entities, establishing subordinate associations between the independent service units and the aggregate service units, and assigning unique service identifiers to all service units; By setting the basic target value value of the service unit, configuring the usage interval boundary threshold of the service unit, defining the target value adjustment coefficient of different usage intervals, and binding the target value adjustment coefficient to the corresponding service unit through the unique service identifier, the target value determination parameter is generated; Set the required fields of the transaction data structure template and generate the transaction data structure template; Setting the layout constraints of the target value result presentation template and generating the target value result presentation template; By identifying a plurality of basic associated object units and a plurality of composite associated object units, establishing hierarchical links between the basic associated object units and the composite associated object units, and configuring target value distribution weights between the associated object units, associated object hierarchical information is generated; Integrating the service level information, the target value determination parameters, the transaction data structure template, the target value result presentation template, and the associated object level information to form configuration information, and generating a configuration version identifier; An integrity check is performed on the configuration information, and the configuration information that passes the integrity check is written into the storage module.

3. The method for automatically determining a numerical value based on hierarchical information according to claim 1, wherein: Receiving raw transaction data to be processed, and formatting and converting the raw transaction data according to the transaction data structure template to generate standardized transaction data, including: Obtaining raw transaction data to be processed through a data receiving interface, and identifying a set of fields in the raw transaction data; Based on the mandatory field set defined in the transaction data structure template, verifying whether the field set contains all mandatory fields; If it is detected that a required field is missing, the missing field content is added to the field set according to the preset field filling strategy; If field redundancy is detected, then based on the field filtering strategy of the transaction data structure template, redundant fields that do not belong to the required field set are removed from the field set; Performing field order sorting and field type conversion on the field set according to the field order and field type strategy in the transaction data structure template; Generate standardized transaction data based on the transformed field set.

4. The method for automatically determining a numerical value based on hierarchical information according to claim 1, wherein: In response to the target value processing trigger instruction, based on the standardized transaction data and in combination with the service level information, the target value determination parameters and the associated object level information, target value attribution and target value allocation processing are performed to generate initial target value details, including: Parsing the target value processing trigger instruction to obtain target value processing parameters, wherein the target value processing parameters include a processing time range and a business type identifier; Determining a target service unit corresponding to the standardized transaction data based on the independent service unit and the aggregated service unit in the service level information and according to the business type identifier; Determining a target associated object unit corresponding to the standardized transaction data based on the basic associated object unit and the composite associated object unit in the associated object hierarchy information and according to the processing time range; Determine the target value of the standardized transaction data according to the basic target value value, the usage interval boundary threshold and the target value adjustment coefficient in the target value determination parameters; The target service unit, the target-related object unit, and the target value are combined to generate an initial target value specification.

5. The method for automatically determining a numerical value based on hierarchical information according to claim 1, wherein: Detect whether there is data configuration missing or data logic conflict in the initial target value details. If so, mark the initial target value details containing data configuration missing or data logic conflict as abnormal target value details and output them, including: According to the service registration list, detecting whether the target service unit included in each record in the initial target value details has been registered; According to the object registration list, detecting whether the target-related object unit contained in each record in the initial target value details has been registered; According to the verification strategy threshold, detecting whether the target value of each record in the initial target value details is within the valid range; When it is detected that the target service unit is not registered in the service registration list, the target associated object unit is not registered in the object registration list, or the target value is not within the valid range defined by the verification policy threshold, the initial target value details of the corresponding record are marked as abnormal target value details; Assigning an abnormality classification code indicating a specific abnormality cause to the abnormal target value details; The abnormal target value details including the abnormal classification code are pushed to the abnormal data review interface.

6. The method for automatically determining a numerical value based on hierarchical information according to claim 1, wherein: For the initial target value details that are not marked as abnormal target value details, target value summary information is generated based on the target value result presentation template, including: Filtering the records in the initial target value details that are not marked as abnormal target value details to obtain a valid target value detail record set; Grouping the effective target value detail record set according to the grouping dimension defined in the target value result presentation template; Applying the summary strategy defined in the target value result presentation template to each set of records after the grouping process to determine the summary value of the target numerical field; According to the layout constraints in the target value result presentation template, the grouping dimension and the summary value are filled into the designated position in the target value result presentation template to generate target value summary information including the filled content.

7. The method for automatically determining a numerical value based on hierarchical information according to claim 1, wherein: Transmitting the target value summary information to a preset target value recording target, and receiving reception status feedback information of the target value recording target, including: Formatting and encapsulating the target value summary information to generate a target value transmission message; Connecting to a preset interface address of the target value recording target through a secure transmission protocol; Sending the target value transmission message to the target value recording target; Receive reception status feedback information returned by the target value record target; The transmission status log is updated according to the reception status feedback information.

8. An automatic numerical value determination device based on hierarchical information, characterized in that: The automatic value determination device based on hierarchical information includes: Configuration management module, used to establish and store service hierarchy information, target value determination parameters, transaction data structure templates, target value result presentation templates and associated object hierarchy information; A data access module is used to receive the original transaction data to be processed, and format and convert the original transaction data according to the transaction data structure template to generate standardized transaction data; a target value calculation module, configured to respond to a target value processing trigger instruction, perform target value attribution and target value allocation processing based on the standardized transaction data and in combination with the service level information, the target value determination parameters, and the associated object level information, and generate an initial target value detail; an abnormality identification module, configured to detect whether there is any data configuration missing or data logic conflict in the initial target value details; if so, mark the initial target value details containing the data configuration missing or data logic conflict as abnormal target value details and output the result; A result summary module is used to generate target value summary information based on the target value result presentation template for the initial target value details that are not marked as abnormal target value details; The result push module is used to transmit the target value summary information to a preset target value recording target and receive reception status feedback information of the target value recording target.

9. A determination device, characterized in that: The determination machine device includes a memory, a processor, and an automated numerical determination program based on hierarchical information stored in the memory and capable of running on the processor. When the automated numerical determination program based on hierarchical information is executed by the processor, the steps of the automated numerical determination method based on hierarchical information as described in any one of claims 1-7 are implemented.

10. A machine-readable storage medium, characterized in that: The storage medium stores an automatic value determination program based on hierarchical information, which, when executed by a processor, implements the steps of the automatic value determination method based on hierarchical information according to any one of claims 1 to 7.

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