CN-121979874-A - Enterprise owner data management and distribution method based on multi-system collaboration

CN121979874ACN 121979874 ACN121979874 ACN 121979874ACN-121979874-A

Abstract

The invention relates to the technical field of data processing, in particular to a business owner data management and distribution method based on multi-system cooperation. The method comprises the steps of synchronously obtaining main data of a plurality of systems about the same entity, associating the main data with product data and real-time equipment metadata, generating consistency labels independent of system rules based on product constraint conditions to serve as objective arbitration references, carrying out collaborative analysis on the system data processing rules according to the labels to identify conflicts, calibrating the main data of the systems with the conflicts to generate and distribute calibrated data matched with the rules, and finally carrying out collaborative and calibration based on system feedback iteration until the similarity of the system rules reaches a set threshold value to achieve quantitative convergence. According to the invention, by introducing objective consistency labels and a quantitative closed-loop mechanism, intelligent, dynamic and consistency collaborative management of cross-system main data is realized.

Inventors

MA CHAOYANG
ZHANG YUANSHENG
GUO CE
GUO QINGJIE

Assignees

北京北矿智能科技有限公司

Dates

Publication Date: 20260505
Application Date: 20260123

Claims (10)

1. A business owner data management and distribution method based on multi-system cooperation is characterized by comprising the following steps: S100, synchronously acquiring main data about the same entity from a plurality of service systems, and correlating the main data with product data and real-time equipment metadata corresponding to the entity to form a cross-system correlated data set; S200, based on constraint conditions defined by the product data, carrying out consistency judgment on the associated data set to generate a consistency label for uniformly representing the entity overall state, wherein the consistency label is independent of data processing rules of each business system; S300, carrying out cooperative analysis on the data processing rules of each service system according to the consistency labels, and identifying rule conflicts to obtain a cooperative analysis result; S400, based on the collaborative analysis result, calibrating the main data distributed to the business system with rule conflict, and generating and distributing calibrated data adapted to the data processing rule of the business system; And S500, performing rule cooperation and data calibration in an iterative manner according to feedback of the service systems to the calibrated data until the similarity of the service systems to the data processing rule of the entity is greater than a set similarity threshold.
2. The method of claim 1, wherein in S200, the consistency tag is generated by: Integrating constraint conditions of the product data with the real-time equipment metadata, and compiling to generate a dynamic constraint compliance rule model of the entity; and inputting the association data set into the dynamic constraint compliance rule model to carry out constraint compliance judgment, and outputting a multi-dimensional state vector as the consistency label.
3. The method according to claim 1 or 2, wherein S300 comprises: carrying out logic mapping and comparison analysis on the multidimensional standardized state represented by the consistency label and rules in a data processing rule base of each service system, and outputting a first analysis result for identifying candidate conflict rules; Comparing the simulated output of each candidate conflict rule with the corresponding state in the consistency label, if the comparison result is a logic contradiction, confirming the candidate conflict rule as a target conflict rule, and outputting a second analysis result containing all target conflict rules; and generating the collaborative analysis result based on the first analysis result and the second analysis result.
4. A method according to claim 3, wherein S400 comprises: judging that the data processing rule of the consistency tag or the specific service system is used as a calibration standard according to a preset collaborative priority strategy; based on the selected calibration standard, the original main data is subjected to logic mapping or semantic alignment processing to generate calibrated data which is matched with the data processing rule of the target service system.
5. The method according to claim 1, characterized in that in S500 the similarity of the data processing rules is calculated by: Processing the same test data set according to the rule serving as the reference and the data processing rule currently effective by each service system respectively, so as to obtain a corresponding reference output result set and an output result set of each service system; and obtaining the quantized similarity value of the data processing rule of each service system by calculating the Jaccard similarity coefficient or cosine similarity between the output result set of each service system and the reference output result set.
6. The method of claim 1, wherein S500 further comprises: recording the difference content and feedback result of each rule cooperation and data calibration to form a cooperation knowledge graph; when the similar entity or similar conflict is processed again, matched experience decision patterns are retrieved and applied from the collaborative knowledge graph to guide the subsequent collaborative and calibration operation.
7. The method of claim 6, wherein S500 further comprises: Training a conflict prediction model based on a history record in the collaborative knowledge graph; When new data is acquired, inputting the new data and the consistency label into the conflict prediction model, wherein the conflict prediction model deduces an output result which is generated by a data processing rule of each business system under the input of the new data based on the collaborative knowledge graph; And carrying out logic comparison on the deduced output result and the consistency label, and if logic contradiction exists, calling a history calibration rule which is matched with the logic contradiction from the collaborative knowledge graph as a recommended calibration rule.
8. The method of claim 1, wherein in the iterative process of S500, if the similarity of a certain service system cannot reach the set similarity threshold after the data processing rule of the service system is calibrated multiple times, an external arbitration command is received through a manual arbitration interface; Taking the external arbitration instruction as a new rule reference, and accordingly performing at least one of the following operations: (a) Correcting the generation logic of the consistency label; (b) And adjusting the collaborative priority policy.
9. The method according to claim 1 or 2, further comprising the step of real-time maintenance of the consistency tag: continuously monitoring the data flow of the real-time equipment metadata; when the critical state parameters of the entity are monitored to exceed the constraint boundaries defined by the product data, the recalculation and dynamic updating of the consistency tags are triggered.
10. The method of claim 1, wherein the entity is an entity having an internal topology, and wherein in S300, if the identified rule conflict relates to association logic among a plurality of nodes in the topology, performing an association calibration operation, wherein the association calibration operation ensures that the generated calibration data meets consistency constraints of the association logic, wherein the calibration data is synchronized to generate calibration data corresponding to all relevant nodes.

Description

Enterprise owner data management and distribution method based on multi-system collaboration Technical Field The invention relates to the technical field of data processing, in particular to a business owner data management and distribution method based on multi-system cooperation. Background In the current enterprise informatization environments such as hospitals, schools, factories and the like, the situation that a plurality of business systems run in parallel exists. These systems are often built in different periods, and different technical architectures and data standards are adopted, so that data semantics among the systems are inconsistent, formats are not uniform, circulation is not smooth, and a data island is formed. In industrial production scenarios, such data inconsistency is particularly prominent, for example, status data (such as "water temperature is high") of the same device may be interpreted as different business meanings (such as "no maintenance" or "no immediate maintenance") in different systems, which may cause contradiction of subsequent production decisions, confusion of process flows, and even product quality risks and safety risks. The method is characterized in that when data are sequentially transmitted among a plurality of systems, the same data can be processed, screened or discarded in different modes due to different business rules and data processing logic built in each system, so that a downstream system can not acquire complete and accurate data, and further the traceability, monitoring and optimization of the production process are affected. In addition, under the real-time collaborative scene of parallel processing, different systems calculate or judge the same data source based on respective rules, and inconsistent or even conflicting processing results can also be generated, so that contradiction of executing actions and collaborative failure are caused. In the prior art, although some main data management methods attempt to improve service adaptability by dynamically expanding a model (for example, a main data model expansion method based on event monitoring and visualization configuration proposed in CN 120578646B), these methods mainly solve the problems of flexibility and response speed of the model itself, and still lack an effective mechanism for managing and distributing main data consistency in a multi-system collaborative scenario. Especially when the business rules of a plurality of systems for the same data are inconsistent, even if the main data model can be dynamically expanded, semantic alignment, rule unification and real-time synchronization of the data in cross-system circulation still cannot be ensured, so that the asset value of enterprise data cannot be fully exerted, and the operation efficiency and decision quality are restricted. Disclosure of Invention Aiming at the technical problems, the invention adopts the following technical scheme: The embodiment of the invention provides a method for managing and distributing enterprise owner data based on multi-system cooperation, which comprises the following steps: S100, synchronously acquiring main data about the same entity from a plurality of service systems, and correlating the main data with product data and real-time equipment metadata corresponding to the entity to form a cross-system correlation data set. And S200, carrying out consistency judgment on the associated data set based on constraint conditions defined by the product data, and generating a consistency label for uniformly representing the entity overall state, wherein the consistency label is independent of data processing rules of each business system. And S300, carrying out collaborative analysis on the data processing rules of each service system according to the consistency labels, and identifying rule conflicts to obtain collaborative analysis results. And S400, based on the collaborative analysis result, calibrating the main data distributed to the business system with rule conflict, and generating and distributing calibrated data adapted to the data processing rule of the business system. And S500, performing rule cooperation and data calibration in an iterative manner according to feedback of the service systems to the calibrated data until the similarity of the service systems to the data processing rule of the entity is greater than a set similarity threshold. The invention has at least the following beneficial effects: According to the invention, the consistency labels independent of the rules of each service system are introduced to serve as unified arbitration references of cross-system data, and the quantifiable similarity threshold is set to serve as a cooperative convergence target, so that the problems of data island and cooperative failure caused by inconsistent data semantics and processing rules among multiple systems are effectively solved. The method realizes the transition from subjective manual alignment to