Enterprise project management method and system based on multi-source heterogeneous data integration

Abstract

The invention relates to the technical field of enterprise project management, and discloses an enterprise project management method and system based on multi-source heterogeneous data integration, and the method comprises the steps: receiving heterogeneous data from a plurality of data sources; classifying the heterogeneous data into different-speed data streams of a high-speed stream, a medium-speed stream and a low-speed stream according to the updating frequency; allocating a logic timestamp for the different-speed data stream; the different-speed data flow is processed based on the logic timestamp, and the logic consistency of data processing is guaranteed; converting, aggregating and analyzing the processed data according to a preset data stream processing logic; the analysis result is applied to enterprise project management decision support; real-time performance, consistency and integrity of data in enterprise project management are achieved, efficiency and decision quality of enterprise project management are improved, and the problems of data islands, data inconsistency and data processing lag in traditional enterprise project management are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise project management, and more specifically, it relates to an enterprise project management method and system based on the integration of multi-source heterogeneous data. Background Art

[0002] With the in-depth promotion of the digital transformation of enterprises, enterprise project management is facing unprecedented data challenges. Traditional enterprise project management methods mainly rely on a single data source or isolated multiple systems, and there is a lack of an effective data integration mechanism between these systems, resulting in the following problems:

[0003] There is a serious "data island" phenomenon within the enterprise. The project management system, code management system, continuous integration / continuous deployment tool, monitoring and logging system, etc. operate independently, and data cannot flow and be shared effectively, making it difficult for managers to obtain a global view of the project and make accurate decisions.

[0004] There are significant differences in the structure, format, and update frequency of heterogeneous data generated by different systems. For example, code commit records may be updated at the minute level, while project plan documents may be updated on a daily or weekly basis, and monitoring system data may be updated at the second level. The existence of this asynchronous data flow makes it difficult for traditional synchronous processing methods to ensure the logical consistency of data processing.

[0005] Most of the data processing methods in the prior art adopt a batch processing mode and cannot meet the requirements for real-time data analysis in enterprise project management. When anomalies or risks occur in a project, managers often cannot obtain relevant information in a timely manner and take measures, resulting in the expansion of problems.

[0006] Existing project management systems lack the ability to adapt to changes in business processes. When the enterprise business process changes, the data processing logic usually needs to be adjusted manually, which not only increases the maintenance cost but also may lead to the disconnection between data processing and actual business.

[0007] When facing large-scale data processing, traditional project management systems often adopt a unified processing strategy and cannot perform differential processing according to the importance and timeliness of data, resulting in unreasonable allocation of system resources and affecting the overall performance.

[0008] Therefore, there is a need for an enterprise project management method and system that can integrate multi-source heterogeneous data, process asynchronous data flows, ensure data logical consistency, support real-time analysis, and adapt to business changes, so as to improve the efficiency and decision-making quality of enterprise project management. Summary of the Invention

[0009] The present invention provides an enterprise project management method and system based on multi-source heterogeneous data integration, which solves the technical problems of data time consistency, insufficient adaptability to business process changes, low real-time processing efficiency, and high complexity of data source integration in the process of multi-source heterogeneous data integration in related technologies.

[0010] The present invention provides an enterprise project management method based on multi-source heterogeneous data integration, including the following steps: Receiving heterogeneous data from multiple data sources, where the heterogeneous data includes data with different structures, formats, and sources; Adaptive classification of the heterogeneous data according to the update frequency and data importance to form a multi-dimensional heterogeneous data stream with differentiated processing priorities; Assigning logical timestamps based on business semantics to the multi-dimensional heterogeneous data stream to construct a temporal consistency framework; Performing intelligent transformation, context-aware aggregation, and predictive analysis on the multi-dimensional heterogeneous data stream based on the logical timestamps, and ensuring the logical integrity across data sources according to the dynamically evolving data stream processing model; Applying the analysis results to the multi-level decision support system of enterprise project management through a visual decision matrix.

[0011] In a preferred embodiment, an enterprise project management method based on multi-source heterogeneous data integration further includes: Real-time monitoring of business process changes in enterprise project management; Automatically perceiving the change patterns and influence scopes of business processes through machine learning algorithms; According to the changes in business processes, adaptively reconstructing the data stream processing logic to achieve the synchronous evolution of the processing logic and business changes.

[0012] In a preferred embodiment, an enterprise project management method based on multi-source heterogeneous data integration further includes: Deploying a lightweight data acquisition adapter with self-learning ability to dock with various data sources and extract data, and the lightweight data acquisition adapter can automatically identify data structure changes and perform adaptation adjustments.

[0013] In a preferred embodiment, the heterogeneous data includes at least one of relational database data, non-relational database data, document data, log data, API interface data, Internet of Things device data, and social media data.

[0014] In a preferred embodiment, an enterprise project management method based on multi-source heterogeneous data integration is executed on a flexible and scalable distributed server cluster, which includes edge data collection servers, centralized data processing servers, and intelligent application servers, and can automatically adjust the computing resource allocation according to the data processing load.

[0015] In a preferred embodiment, an enterprise project management method based on multi-source heterogeneous data integration is built on an event-driven distributed stream processing framework, which includes a data stream engine with a fault tolerance mechanism and can ensure the consistency and integrity of data processing in case of node failures.

[0016] In a preferred embodiment, the data sources include at least one of an enterprise internal project management system, a code management system, a continuous integration and deployment tool, a monitoring and logging system, a communication and collaboration tool, a document management system, an enterprise resource planning system, and an external market data system.

[0017] In a preferred embodiment, the allocation of logical timestamps adopts a multi-level priority algorithm, and dynamically assigns priorities by comprehensively considering the business criticality, update frequency, data dependency relationships, and historical processing patterns of the data stream.

[0018] In a preferred embodiment, an enterprise project management method based on multi-source heterogeneous data integration In a preferred embodiment, it further includes: Implementing a differential processing strategy for multi-dimensional asynchronous data streams, achieving millisecond-level response for critical business data, and at the same time providing a resource-efficient batch processing mechanism for non-critical data.

[0019] In a preferred embodiment, an enterprise project management system based on multi-source heterogeneous data integration, which is used to execute an enterprise project management method based on multi-source heterogeneous data integration, includes: An intelligent data receiving module, which is used to receive heterogeneous data from multiple data sources, and the heterogeneous data includes data with different structures, formats, and sources; An adaptive data classification module, which is used to adaptively classify the heterogeneous data according to the update frequency and data importance, and form a multi-dimensional heterogeneous data stream with differential processing priorities; A semantic timestamp allocation module, which is used to allocate logical timestamps based on business semantics for the multi-dimensional asynchronous data stream and construct a temporal consistency framework; An intelligent analysis and processing module, which performs intelligent transformation, context-aware aggregation, and predictive analysis on the multi-dimensional asynchronous data stream based on the logical timestamp, and ensures the logical integrity across data sources according to the dynamically evolving data stream processing model; The multi-level decision support module is used to apply the analysis results to the multi-level decision support system of enterprise project management through a visual decision matrix.

[0020] The beneficial effects of the present invention are as follows: Consistency of data processing logic and adaptability of business processes: By classifying heterogeneous data into multi-dimensional heterogeneous data streams and assigning logical timestamps, the present invention solves the problem of data time misalignment, ensuring the logical consistency of data processing; at the same time, it can monitor business process changes in real time and automatically adjust the data processing logic, enabling the system to quickly adapt to changes in business requirements and maintain the consistency between data analysis and actual business.

[0021] Real-time processing and system architecture optimization: The system architecture based on the distributed stream processing framework realizes the real-time processing of asynchronous data streams, supports the real-time monitoring and quick decision-making of enterprise projects, and avoids the latency problem of traditional batch processing methods; at the same time, the adopted distributed server cluster architecture and the priority-based logical timestamp assignment mechanism enable the system to have good scalability and fault tolerance, ensuring the continuity and reliability of data processing.

[0022] Data source integration and elimination of enterprise data islands: Through lightweight data acquisition adapters, the plug-and-play of data acquisition components is realized, greatly reducing the development and maintenance costs of integrating new data sources; at the same time, it integrates the data of multiple systems within the enterprise, breaks the data islands in traditional enterprise project management, realizes the sharing and circulation of data, and provides a data basis for comprehensively understanding the project status.

[0023] Optimization of resource allocation and differential processing strategy: Based on the analysis results of multi-source heterogeneous data, it can more accurately predict project resource requirements, optimize resource allocation strategies, and improve the utilization efficiency of enterprise resources; by implementing a differential processing strategy for multi-dimensional heterogeneous data streams, it achieves millisecond-level response to key business data, while providing an efficient batch processing mechanism for non-critical data, optimizing resource utilization while ensuring system performance.

[0024] Improvement of decision-making quality and guarantee of system reliability: By converting, aggregating, and analyzing the processed data, it provides comprehensive, accurate, and timely decision support information for enterprise project management, helping managers make more scientific and reasonable decisions and reducing decision-making risks; at the same time, the adopted event-driven distributed stream processing framework has a perfect fault tolerance mechanism, which can ensure the consistency and integrity of data processing in case of node failures, improving the reliability and stability of the system. Brief Description of the Drawings

[0025] Figure 1 is a flowchart of an enterprise project management method based on the integration of multi-source heterogeneous data of the present invention; Figure 2It is a line chart showing the change of the system resource utilization rate of the present invention with the increase of data flow; Figure 3 It is a scatter chart showing the relationship between the business process change adaptation time and the change complexity of the present invention. Detailed implementation manners

[0026] Now, the subject matter described herein will be discussed with reference to exemplary embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein. Without departing from the scope of protection of the content of this specification, changes can be made to the functions and arrangements of the elements discussed. Each example can omit, substitute or add various processes or components as needed. Additionally, the features described in some examples can also be combined in other examples.

[0027] In at least one embodiment of the present invention, an enterprise project management method based on multi-source heterogeneous data integration is disclosed, as Figure 1 shown, which includes the following steps: Step 1, receiving heterogeneous data from multiple data sources, where the heterogeneous data includes data with different structures, formats, and sources; Specifically, it includes the following steps: Step 1.1, creating an adapter registry for managing various data source adapters; The registry includes an adapter metadata database, an adapter instance manager, and an adapter monitoring component.

[0028] The adapter metadata database stores the configuration information of various adapters, including connection parameters, data structure descriptions, and conversion rules; The adapter instance manager is responsible for the creation, start, stop, and destruction of adapters; The adapter monitoring component monitors the running status and performance metrics of each adapter in real time.

[0029] The adapter registry can be implemented using a centralized or distributed architecture.

[0030] In a centralized architecture, a single registry server manages all adapters; In a distributed architecture, multiple registry nodes work together to improve the reliability and scalability of the system.

[0031] In some embodiments, the registry can also support the automatic discovery and registration function of adapters, simplifying the system configuration and deployment process.

[0032] Step 1.2, implementing the standard interface of the data source adapter, including a connection interface, a data extraction interface, a metadata acquisition interface, and a health check interface.

[0033] Among them, the connection interface defines methods for establishing and maintaining a connection with the data source; The data extraction interface defines methods for obtaining data from the data source, supporting full extraction and incremental extraction; The metadata acquisition interface defines methods for obtaining the structural information of the data source; The health check interface defines methods for checking the running status of the adapter.

[0034] In addition, the design of the standard interface follows the minimization principle to ensure that the interface is concise and easy to implement.

[0035] For example, the data extraction interface may include the following core methods: For obtaining full data; For obtaining incremental data after a specified timestamp; For obtaining data according to filtering conditions.

[0036] In some embodiments, additional methods may also be extended, such as For obtaining real-time data streams, or For querying the set of features supported by the adapter.

[0037] Step 1.3, build adapter plugins for different types of data sources based on the standard interface, including relational database adapters, API interface adapters, message queue adapters, file system adapters, and log system adapters, etc.

[0038] Each adapter optimizes the data extraction logic for a specific type of data source. For example, the relational database adapter uses transaction log parsing technology to achieve efficient incremental data extraction; the API interface adapter implements request throttling and caching mechanisms to avoid API rate limiting caused by frequent requests.

[0039] Therefore, in this way, efficient acquisition of various data sources can be ensured.

[0040] Step 1.4, build a unified data collection bus to receive the data collected by each adapter and perform preliminary classification.

[0041] The unified data collection bus is implemented using a distributed message queue, supporting data partitioning and data pipeline processing to ensure high throughput and low latency.

[0042] The bus marks the received data with timestamps, source information, and performs data quality assessment, providing a basis for subsequent processing.

[0043] Step 2, adaptively classify the heterogeneous data according to the update frequency and data importance to form a multi-dimensional heterogeneous data stream with different processing priorities; Specifically, it includes the following steps: Step 2.1: Classify the uniformly collected data streams, and divide the data streams into fast streams , medium-speed streams and slow streams .

[0044] The division basis is the specific update frequency range as follows: The update period of the fast stream is in the second level to the minute level, such as system monitoring data and real-time logs; The update period of the medium-speed stream is in the hour level to the day level, such as task status updates and code submission records; The update period of the slow stream is in the week level to the month level, such as financial data and performance evaluation data.

[0045] In some embodiments, the data stream classification can adopt an adaptive threshold method to dynamically adjust the classification threshold according to the historical update statistical information of the data stream.

[0046] For example, the system can calculate the average update frequency of each data stream in different time periods, and combine the business importance and access pattern of the data stream to automatically classify the data stream into the most suitable category. This method can enable the system to adapt to seasonal changes or long-term trend changes in the data stream update pattern.

[0047] Step 2.2: Establish a dependency graph between data streams , where represents the data stream set, represents the dependency relationship between data streams.

[0048] For data streams with a dependency relationship , the system maintains the strength weight and time sensitivity of the dependency relationship for subsequent calculation of logical timestamps.

[0049] The construction of the dependency graph can adopt a combination of static definition and dynamic discovery.

[0050] The static definition of the dependency relationship is based on pre-configured business rules to clearly specify the dependency relationship between data streams; The dynamic discovery of the dependency relationship automatically identifies potential dependency relationships by analyzing the access patterns and correlations between data streams.

[0051] For example, the system can apply an association rule mining algorithm to analyze the temporal relationship of data streams in business processing, discover frequently co-occurring data streams and establish dependency relationships.

[0052] Step 2.3, apply the logical timestamp algorithm to assign a logical timestamp to each data item.

[0053] The calculation formula for the logical timestamp is: ; where represents the logical timestamp of the data item, which is a unified time identifier assigned by the system to the data item and is used to ensure the logical consistency of data at different rates; represents the physical acquisition time of the data item, that is, the time point when the data is actually acquired, which is the actual timestamp recorded by the system; represents the set of logical timestamps of other data items that have a dependency relationship with this data item and is used to reflect the time sequence dependency relationship between data items; represents the logical timestamp calculation function, which is used to comprehensively consider the physical time and the dependency relationship to generate the final logical timestamp value.

[0054] The specific calculation is as follows: where is the physical time weight factor, and its value range is [0, 1], which is dynamically adjusted according to the data stream type. When is close to 1, the logical timestamp is more dependent on the physical acquisition time; when is close to 0, the logical timestamp is more dependent on the timestamps of related data items; is the physical acquisition time of the data item, that is, the time point when the data is actually acquired; is the set of logical timestamps of other data items that have a dependency relationship with this data item; is the calculated logical timestamp of the data item, which is used to ensure the logical consistency of data at different rates; is the comprehensive function of the dependency timestamp, which is used to calculate the weighted comprehensive value of the timestamps of dependent data items, and the calculation is: ; where represents the comprehensive function of the dependency timestamp, which is used to calculate the sum of the weighted timestamps of all dependent data items; represents the summation of all data stream pairs with a dependency relationship, represents the data stream to the data stream dependency relationship; represents the data stream to dependency relationship strength weight, which is used to quantify the importance of the dependency relationship; represents the data stream to The time sensitivity, which is used to measure the sensitivity of dependencies to time changes; Indicates the dependent data stream The logical timestamp, that is, the logical time value of the upstream data item on which the current data item depends.

[0055] Step 2.4, construct a data stream buffer management mechanism to coordinate the processing of data streams with different rates.

[0056] For fast streams , adopt a sliding window processing mechanism to calculate the logical timestamp of the latest data in real time; For medium-speed streams , adopt a time-partitioned buffer to aggregate data according to the configured time period; For slow streams , adopt a combination of long-term storage and incremental update to ensure data consistency and availability.

[0057] In the specific application scenario of enterprise project management, the implementation method of the heterogeneous speed data stream adaptive processing algorithm is as follows: Taking software development project management as an example, the system simultaneously processes data sources with different rates: Fast streams include system performance monitoring data (updated per second) and development environment status data (updated per minute); Medium-speed streams include task status updates (updated hourly or daily) and code submission records (updated irregularly); Slow streams include weekly report data and monthly performance evaluation data.

[0058] The logical timestamp algorithm assigns reasonable physical time weight factors to data with different rates according to the requirements of project management : For real-time monitoring scenarios, the value of fast streams is close to 1, and physical time is given priority; For progress tracking scenarios, the value of medium-speed streams is about 0.6, balancing physical time and dependencies; For performance evaluation scenarios, the value of slow streams is about 0.3, and dependencies are considered more.

[0059] During project milestone reviews, the system needs to integrate data at different rates for analysis. At this time, through the logical timestamp algorithm, the system can ensure the logical consistency of real-time monitoring data (fast stream), daily task completion status (medium stream), and monthly plan completion status (slow stream), avoiding analysis biases caused by differences in data update frequencies. For example, when evaluating the completion quality of a development task, the system correlates the status update of the task (medium stream) with relevant performance monitoring data (fast stream) and quality assessment reports (slow stream) through logical timestamps to provide a comprehensive evaluation view.

[0060] Step 3: Assign logical timestamps based on business semantics to multi-dimensional heterogeneous data streams and construct a temporal consistency framework; Specifically, it includes the following steps: Step 3.1: Construct a unified data model for standardizing heterogeneous data from different sources; The unified data model is represented in a graph structure and contains two basic elements: entity nodes and relationship edges.

[0061] Entity nodes represent business objects (such as projects, tasks, resources, etc.), and relationship edges represent the associations between entities (such as subordination relationships, dependency relationships, etc.).

[0062] Each entity node and relationship edge contains a set of attributes for storing specific business data.

[0063] The unified data model can be implemented in different structures. In addition to the above graph structure, it can also adopt a hierarchical structure (tree model), a relational structure (table model), or a hybrid structure, and the most suitable model structure is selected according to specific business requirements and data characteristics.

[0064] For example, for organizational structure data with clear hierarchical relationships, a hierarchical structure can be adopted; for transaction-centered financial data, a relational structure can be adopted.

[0065] In some embodiments, the unified data model can also support the dynamic expansion of the model, allowing new entity types and relationship types to be added during system operation.

[0066] Step 3.2: Implement an incremental data stream conversion algorithm that dynamically selects the optimal conversion strategy according to the nature of data changes.

[0067] The incremental data stream conversion algorithm includes the following calculation steps: For the input data stream , calculate its change set with the data stream processed last time: ; where Represents the data stream input at the current moment, containing the latest data set; Represents the historical data stream processed last time, serving as a comparison benchmark; Represents the change set, that is, the different part between the current data stream and the historical data stream; Represents the set difference operation, used to extract the data items in the current data stream that are different from the historical data stream.

[0068] For the change set Classify it into newly added data , updated data and deleted data ; Apply conversion operations respectively for different types of changed data: For newly added data , apply the complete conversion function ; For updated data , apply the differential conversion function ; For deleted data , apply the reverse conversion function ; Merge the conversion results with the existing unified data model to obtain the updated data model: ; Among them, Represents the updated data model, that is, the latest unified data model after the conversion process; Represents the data model before update, that is, the unified data model of the previous version; Represents the model merge operation, used to integrate the converted changed data with the existing model; Represents the combined result of various conversion functions, including the comprehensive result after applying the conversion functions for newly added data, updated data, and deleted data respectively; Represents the change set, that is, the different part between the current data stream and the historical data stream.

[0069] In the scenario where data changes frequently but with small amplitude, the following optimized variant algorithm can be optionally adopted: To improve the processing efficiency of small-scale and frequent changes, the system can implement a change batch processing mechanism. This mechanism collects multiple small-scale changes within a short period to form a change batch , and then performs the merge processing: ; Among them, Represents the merged batch change set, which is the result of merging multiple small-scale changes; An operator for merging change sets, used to integrate multiple change sets into one set; Indicates starting from the first change set; Indicates the total number of change sets; Indicates the th change set, representing a single data change; Indicates a change batch, which is a collection of multiple small-scale changes; 、 、 Respectively indicate the 、 、 th independent change sets collected within a short period of time; Indicates the number of changes included in the batch.

[0070] The merged change set Is then processed according to the above standard algorithm, significantly reducing the number of processing times and system overhead.

[0071] Step 3.3, construct a data transformation rule engine to support declarative rule definition and automatic rule derivation.

[0072] The rule engine includes a rule repository, a rule executor, and a rule learning module; The rule repository stores predefined transformation rules and rule templates; The rule executor is responsible for interpreting and executing transformation rules; The rule learning module automatically derives new transformation rules based on historical transformation data to improve system adaptability.

[0073] Step 3.4, establish a data quality assessment and repair mechanism to monitor data quality problems during the transformation process in real time and automatically repair them; Data quality assessment includes integrity check, consistency check, accuracy check, and timeliness check.

[0074] When data quality problems are found, the system automatically repairs them according to the preset repair strategy and records the repair log for subsequent analysis and improvement.

[0075] In the actual application scenario of enterprise project management, the implementation method and application examples of the incremental data flow transformation algorithm are as follows: Taking the enterprise project management environment of multi-system integration as an example, the enterprise uses multiple systems such as a project management system, a code management system, a CI / CD system, and a human resources system at the same time. When the incremental data flow transformation algorithm processes the data of these heterogeneous systems, different strategies are adopted according to the nature of data changes: When the task status in the project management system changes from "in progress" to "completed" (belonging to updated data ), the system only processes the status change part and applies the differential transformation function Convert the status change into the corresponding update in the unified data model. Compared with processing all task data in full volume, this incremental processing method significantly reduces the amount of calculation and data transmission.

[0076] When a new code repository is added (belonging to the newly added data ), the system applies the complete transformation function to convert the complete metadata and initial code analysis results of the repository into new nodes and relationships in the unified data model. In this case, since it is completely new data, the system needs to perform a complete conversion process.

[0077] When a project member leaves the company (belonging to the deleted data ), the system applies the reverse transformation function to safely remove the association between the member and the project from the unified data model while retaining the historical contribution records. This processing method ensures the integrity and consistency of the data.

[0078] Step 4: Based on the logical timestamp, perform intelligent transformation, context-aware aggregation, and predictive analysis on the multi-dimensional allometric data stream, and ensure the logical integrity across data sources according to the dynamically evolving data stream processing model; Specifically, it includes the following steps: Step 4.1: Build a business process monitoring system to collect and analyze business activity logs in real time; The system includes a log collector, a process recognition engine, and a change detector.

[0079] The log collector collects activity logs from each business system; The process recognition engine constructs the current business process model based on the collected logs; The change detector detects changes in the business process by comparing process models at different time points.

[0080] The detailed composition of the business process monitoring system is as follows: The log collector adopts a distributed collection architecture, including a log proxy, a log transmission channel, and a log aggregation service.

[0081] The log proxy is deployed on each business system and is responsible for collecting and preprocessing local logs; The log transmission channel is implemented based on a message queue to ensure the reliable transmission of log data; The log aggregation service receives all log data and performs unified storage and indexing.

[0082] The process recognition engine extracts the process model from the business activity logs based on process mining technology.

[0083] The engine includes a preprocessing module, a process discovery module, and a process optimization module.

[0084] The preprocessing module cleans, filters, and transforms the original logs; The process discovery module constructs a process model from the activity sequence by applying an improved Alpha algorithm; The process optimization module improves the process model by merging similar paths, removing noise, and calculating frequency information.

[0085] The change detector identifies changes in the business process by calculating the similarity and difference of process models. The detector includes a model comparison algorithm, a change classifier, and a change impact analyzer.

[0086] The model comparison algorithm calculates the structural and attribute differences between two process models; The change classifier classifies the detected changes into types such as new nodes, deleted nodes, and path changes; The change impact analyzer evaluates the potential impact degree of the changes on data flow processing.

[0087] Step 4.2, establish the mapping relationship between the business process and data flow processing; The mapping relationship is represented by a function: ; where represents the data flow processing configuration, which is a complete set of rules on how the system processes, transforms, and routes data; represents the business process model, which contains a structured representation of business activity nodes, decision points, transfer paths, and their relationships; represents the data source characteristics, including a parameter set describing the attributes of the data source such as data format, update frequency, reliability, integrity, etc.; represents the context environment parameters, which include environmental factors affecting data flow processing such as system load, network status, user preferences, and security policies.

[0088] The specific mapping calculation method is: ; where represents the data flow processing configuration; represents the data routing rule set, which defines how data flows in the system, including rules such as the source, destination, transmission path, and conditional branches of the data; represents the data conversion rule set, which defines how to convert the original data into a standard format, including rules such as field mapping, format conversion, data cleaning, and verification; represents the data aggregation rule set, which defines how to merge and aggregate relevant data, including rules such as data grouping, summary calculation, time window aggregation, and correlation merging. Represents data processing priority rules, defining the processing order of different data streams, including a priority allocation mechanism based on business importance, timeliness, resource consumption, and dependencies.

[0089] Each rule set is generated by mapping nodes and edges in the business process model For example, decision points in the business process are mapped to data routing rules, and business activities are mapped to data transformation rules.

[0090] Step 4.3, implement an adaptive data stream reconfiguration mechanism. When a business process change is detected, the system performs data stream reconfiguration according to the following steps: Identify the changed business process nodes and relationships, and calculate the change set ; Calculate the data stream processing configuration that needs to be updated according to the mapping function: ; Among them, represents the data stream processing configuration that needs to be updated, that is, the set of data processing rules that need to be adjusted due to business process changes; represents the mapping function, which is used to convert business process changes into corresponding data stream processing configuration changes; represents the change set of the business process, including newly added, modified, or deleted business process nodes and relationships; represents data source characteristics, including a parameter set describing data source attributes such as data format, update frequency, reliability, integrity, etc.; represents context environment parameters, including environmental factors such as system load, network status, user preferences, and security policies that affect data stream processing.

[0091] Generate a data stream reconfiguration plan, including a configuration update sequence and verification test cases; Execute the data stream reconfiguration according to the plan, and monitor the status and impact of the reconfiguration process to ensure system stability.

[0092] Step 4.4, build a visual process data association monitoring tool to provide managers with a visual view of business processes and data stream processing, supporting manual intervention and adjustment.

[0093] The tool displays the current business process model, data stream processing configuration, and their mapping relationships, and at the same time provides historical change records and performance metric analysis to assist managers in understanding the system operation status and optimization directions.

[0094] In the case of enterprise organizational change, application examples of data stream processing technology with business process awareness are as follows: A manufacturing enterprise transforms from a functional organization to a matrix organization, resulting in the project approval process changing from the original hierarchical approval to a parallel approval mode.

[0095] By analyzing recent activity logs, the business process monitoring system detects a significant change in the approval path: the original serial path of "Project Manager → Department Manager → Director → Vice President" has changed to a parallel hybrid path of "Project Manager → (Department Manager, Functional Expert) → Comprehensive Review Committee".

[0096] The change detector identifies this major process change and triggers the data flow reconfiguration mechanism. The system calculates the data flow processing configurations that need to be updated based on the mapping relationship: the data routing rule changes from serial forwarding to parallel distribution, the data aggregation rule changes from hierarchical summarization to a comprehensive scoring mechanism, and the processing priority changes from hierarchical priority to time priority.

[0097] The data flow reconfiguration plan is executed in two phases: In the first phase, a new approval path and data processing logic are established but not activated yet; In the second phase, the new process is enabled, and the old process is retained to handle in - progress approval projects, while all new projects follow the new process.

[0098] Through this smooth transition, the processing of project management data during the enterprise organizational change maintains continuity and consistency, avoiding data processing chaos and business interruption during the change in traditional systems.

[0099] Step 5, apply the analysis results to the multi - level decision - making support system for enterprise project management through a visual decision matrix; Specifically, it includes the following steps: Step 5.1, design a process data consistency model to define the consistency constraints between the business process state and the data flow processing state.

[0100] The consistency model is expressed by the following formula: ; Among them, represents the consistency constraint function between the business process state and the data flow processing state; represents the business process state at time point t, that is, a complete description of the configurations, rules, and executions of all business processes in the system at a specific time point t; represents the data flow processing state at time point t, that is, a complete description of the routing rules, transformation logics, processing priorities, and execution states of all data flows in the system at a specific time point t; represents the set of nodes in the system, including all servers, application instances, and processing units participating in data processing and business process execution; represents the node The view of state X, i.e., the nodes The information perceived about state X, including the nodes All attributes and parameters of state X that are accessible; Denotes an equivalence relation, indicating that two views are semantically identical, ensuring that the nodes' understanding of the business process and data flow remains synchronized; Denotes that it holds for all nodes in the system, ensuring global consistency This consistency model ensures that each node in the system has an equivalent view of the business process state and data flow processing state, avoiding data processing errors caused by inconsistent states.

[0101] Step 5.2, Implement a distributed consensus algorithm to achieve global consensus when the business process changes; The distributed consensus algorithm is extended based on the two-phase commit protocol and includes the following steps: The process change coordinator sends a pre-commit message to all participating nodes, including a description of the business process change and a data flow reconfiguration plan ; Each node verifies the feasibility of the change and returns a ready or reject response to the coordinator; If all nodes return ready, the coordinator sends a commit message, otherwise an abort message; Finally, each node executes or abandons the change according to the coordinator's decision and returns the execution result.

[0102] Step 5.3, Build a business process version management system to support the parallel operation of old and new business processes; In addition, the version management system includes a process version library, a version router, and a version compatibility checker.

[0103] The process version library stores different versions of the business process model and the corresponding data flow processing configurations; The version router directs the data flow to the appropriate version of the processing logic based on data characteristics and context information; The version compatibility checker ensures that data exchange between different versions does not result in data loss or errors.

[0104] Step 5.4, Build a process data switch manager responsible for coordinating the smooth transition between old and new business processes; The switch manager implements the following functions: Data migration planning: Calculate the data mapping relationship between the old and new processes and generate a data migration plan; State synchronization control: Maintain the state synchronization of the old and new processes during the switch to ensure data consistency; Rollback mechanism: In case of an exception during the switching process, it can safely roll back to the previous stable state; Switching progress monitoring: Real-time monitoring of the progress and status of the switching process, providing visualization display and alarm functions.

[0105] The specific implementation and details of the process data synchronization protocol are as follows: The distributed consensus algorithm is implemented based on an improved version of the two-phase commit protocol, adding a pre-verification phase and a dynamic participant management mechanism.

[0106] The pre-verification phase conducts a comprehensive test on the configuration change before formal submission to ensure the feasibility of the change; The dynamic participant management mechanism allows the dynamic addition or reduction of participating nodes during the consensus process, improving the flexibility and fault tolerance of the system.

[0107] The algorithm decides the acceptance or rejection of the configuration change through a voting mechanism, adopting the weighted majority principle, and key nodes have a higher voting weight.

[0108] The implementation of the consistency model adopts a hierarchical state synchronization mechanism: ; Among them, represents the consistency constraint function between the business process state and the data flow processing state implemented by the hierarchical state synchronization mechanism; represents the business process state at time point t, including the configuration, rules, and execution status of all business processes; represents the data flow processing state at time point t, including the routing rules, transformation logic, and processing status of all data flows; represents the set of all nodes in the system, including all participating servers and application instances; represents the node 's view or understanding of the business process state; represents the node 's view or understanding of the data flow processing state; represents an equivalence relationship, ensuring that the two views are semantically exactly the same; represents that this equivalence relationship must be satisfied for all nodes in the system.

[0109] The implementation of this model adopts a hierarchical state synchronization mechanism: At the bottom layer, a vector clock is used to track the status updates of each node; At the middle layer, event sourcing technology is adopted to record all state change events, supporting state reconstruction and backtracking; At the upper layer, visual Figure 1 consistency checking is implemented to ensure that the views of all nodes on the business process state and the data flow processing state are equivalent.

[0110] The specific implementation of the business process version management system includes: the process version library stores process models of different versions using a graph database, supporting version branching, merging, and comparison; The version router is implemented based on a context-aware rule engine, dynamically selecting the processing version according to factors such as data characteristics, processing stages, and system load; The version compatibility checker evaluates the data exchange compatibility between different versions through semantic equivalence analysis and data flow simulation tests.

[0111] In the scenario of multi-site collaborative project management, the application example of the process data synchronization protocol is as follows: The R & D project of a multinational enterprise involves collaborative work among three R & D centers in Asia, Europe, and North America. Each center has its own project management processes and data processing logics. The enterprise decides to unify the global R & D process and maintain business continuity during the transition period.

[0112] The application of the process data synchronization protocol in this scenario includes: Nodes in each location reach an agreement on the new globally unified process through a distributed consensus algorithm, and at the same time determine the length of the transition period and the switching strategy; The business process version management system configures process variants adapted to local characteristics for each region, while maintaining compatibility with the global standard process; The process data switching manager formulates personalized transition plans for each region, determining the best switching timing according to the project cycles and business characteristics of each region.

[0113] During the implementation of the new process in the Asian R & D center, a critical ongoing project needs to continue using the old process until completion. At this time, the process data synchronization protocol ensures that the data processing of this project proceeds normally under the old process, and at the same time, through the status synchronization control mechanism, the project status data is kept consistent in the new and old systems, achieving the continuity and consistency of data processing during the business process change.

[0114] Through the implementation of the above five main steps, the technical solution of this application realizes an enterprise project management method based on the integration of multi-source heterogeneous data, effectively solving technical problems such as data time consistency, business process change adaptability, real-time processing efficiency, and data source integration complexity faced in enterprise project management.

[0115] Application example of this implementation method: The following shows the actual application process and effect of the method of this application through a case of the transformation of the project management system of a multinational software R & D enterprise. This enterprise manages multiple software product lines at the same time, involves multiple departments such as R & D, testing, and operation and maintenance, uses multiple sets of information systems, and faces typical challenges in the integration of multi-source heterogeneous data.

[0116] Application scenario description: The enterprise has 6 R & D centers globally, runs 35 software product projects simultaneously, and has approximately 2,000 employees. In the process of project management, it faces the following specific challenges: Diversity of data sources: The enterprise uses 12 different information systems, including JIRA task management system, GitLab code management platform, Jenkins CI / CD system, SonarQube code quality analysis tool, Prometheus monitoring system, employee attendance system, financial system, customer feedback system, etc. These systems adopt different data structures and formats, with multiple database types (MySQL, MongoDB, PostgreSQL, etc.) and data exchange methods (API, message queue, file export, etc.).

[0117] Significant differences in data update frequencies: System monitoring data is updated every 10 seconds, code commit records are generated irregularly (dozens of times per hour on average), task status is updated several times a day, while financial data and personnel assessment data are updated weekly or monthly. Traditional unified batch processing methods either delay the processing of real-time data or cause resource waste.

[0118] Frequent business process adjustments: The enterprise adjusts its product development strategy every quarter according to market demands and accordingly changes project management processes, such as approval processes, resource allocation processes, etc. These changes require manual adjustment of data processing logic, resulting in the project management system often lagging behind the actual business process by 1 - 2 weeks, causing data inconsistencies and decision-making deviations.

[0119] System performance bottleneck: With the growth of projects and data volume, the original batch processing-based data integration solution can no longer meet the performance requirements. The average data update delay reaches 4 hours, and exceeds 12 hours during peak periods, seriously affecting the ability of real-time decision-making and anomaly warning.

[0120] The enterprise hopes to build a unified project management platform that can integrate all heterogeneous data sources in real time, automatically adapt to business process changes, and provide a comprehensive and accurate view of project status and decision-making support.

[0121] Implementation examples of core steps: Construction of a lightweight multi-source data collection adapter system: The enterprise first built an adapter registry, which was deployed in 6 R & D centers using a distributed architecture, and set up a main center as a coordination node. The registry was configured with the following key components: Adapter meta-database: Using a distributed key-value storage system to record the configuration information of each adapter, including: Adapter ID: A unique identifier in the form of "AD-2023-JIRA-001"; Connection parameters: Data source access credentials, access address, access method (API / database / message queue); Data structure description: Field mapping relationships, data type conversion rules; Collection frequency configuration: Collection interval settings dynamically adjusted according to data update frequency; Standard interface implementation: Based on the abstract factory pattern, a general adapter interface is designed, covering 4 core functions: Connection management: Establish, maintain, and release connections to the data source; Data extraction: Supports full extraction, incremental extraction, and change capture; Metadata acquisition: Automatically discovers and maps the data source structure; Health monitoring: Self-diagnosis and recovery capabilities; Adapter plugin development: Specialized adapter plugins have been developed for 12 information systems, and each plugin is optimized for the characteristics of a specific data source: For JIRA and GitLab systems, the adapter implements API rate limiting control and response caching to effectively handle API usage limits; For relational databases such as MySQL, the adapter uses binlog parsing technology to achieve zero-latency change capture; For analysis tools such as SonarQube, the adapter implements an incremental data extraction algorithm to obtain only new or changed analysis results; For financial and human resources systems, the adapter integrates file monitoring and automatic parsing capabilities to process regularly generated spreadsheet reports; During actual deployment, the enterprise also developed an Adapter Development Kit (ADK), which significantly simplifies the development process of new adapters. Through this toolkit, a developer can complete the adapter development for a new system in an average of only 4 hours, while it would take an average of 3 to 5 days using traditional integration methods.

[0122] All adapters send the collected data to a unified data collection bus, which is implemented based on a distributed message queue and configured as a multi-level topic structure to achieve preliminary classification of data.

[0123] Metadata tags are automatically added when data enters the bus, including: Data source identifier: Records the source system of the data; Physical timestamp: Records the exact time when the data was collected; Collection node information: Records the server node that performs the collection; Data quality indicators: Records the preliminary evaluation results of integrity and accuracy.

[0124] Application of Allometric Data Flow Adaptive Processing Algorithm: Based on the characteristics of data update frequency, the enterprise divides the collected data flow into three levels: Fast Flow Configuration: Covered data: System monitoring data, user activity logs, build pipeline status; Update frequency: 10 seconds to 5 minutes; Processing strategy: Real-time processing, with a sliding window size of 30 seconds; Physical time weight ( ): 0.85, giving priority to physical time; Medium-speed Flow Configuration: Covered data: Task status changes, code commit records, quality analysis results; Update frequency: 1 hour to 1 day; Processing strategy: Near-real-time processing, with a time partition of 1 hour; Physical time weight ( ): 0.6, balancing physical time and dependencies; Slow Flow Configuration: Covered data: Weekly report data, financial data, personnel evaluation data; Update frequency: 1 week - 1 month; Processing strategy: Periodic processing, maintaining long-term data consistency; Physical time weight ( ): 0.3, giving priority to dependencies; The enterprise analyzes the data flow dependency relationships of each product line project and constructs a dependency relationship graph . The following are some dependency relationship examples of the core product A: Dependency relationship 1: Task completion status → Build status : Dependency strength weight ; Time sensitivity ; Indicates that the build process should be triggered after the task is completed; Dependency relationship 2: Code commit → Quality analysis : Dependency strength weight ; Time sensitivity ; Indicates that quality analysis should be performed after the code is committed; Dependency 3: Build Status → Deployment Status : Dependency Strength Weight ; Time Sensitivity ; It means that the deployment process should be triggered after the build is completed; Based on these configurations and the dependency graph, the system calculates the logical timestamp for each data item. Taking a code submission event as an example, the calculation process is as follows: Physical Acquisition Time: ; Logical Timestamp of Dependent Data: ; According to the formula Calculate: (Medium-speed Flow Weight); (Comprehensive Result of Dependent Timestamps); Final Logical Timestamp ; This logical timestamp mechanism enables the system to coordinate data processing at different rates. For example, when analyzing the release status of a certain product version in a certain week, the system can ensure that all the data used (from real-time monitoring data to weekly report data) is consistent in logical time, avoiding inconsistent analysis results caused by different data update frequencies in traditional systems.

[0125] In practical applications, enterprises have configured corresponding processing mechanisms for three types of data streams: The fast stream uses a memory-based stream processing engine and configures a sliding window mechanism, and the window size is dynamically adjusted according to the characteristics of the data stream, ranging from 10 seconds to 5 minutes; The medium-speed stream uses a near-real-time batch processing engine and configures a micro-batch processing mechanism, and the batch interval is 15 minutes; The slow stream uses periodic batch processing, combined with an incremental update mechanism, and sets the processing period to once a day; This hierarchical processing mechanism not only ensures the logical consistency of data but also significantly optimizes the system resource utilization rate, avoiding resource waste or reduced real-time performance caused by applying a unified processing period to all data.

[0126] Application of the incremental data stream conversion algorithm: Enterprises have built a unified data model based on a graph structure, including the following core entities and relationships: Core Entity Nodes: Project: Represents a product development project; Task: Represents development tasks and work items; Member: Represents project team members; Code: Represents source code and repositories; Build: Represents CI / CD build tasks; Resource: Represents project resources; Relationship edges: Contains: The project contains tasks; Responsible: Members are responsible for tasks; Created: Members create code; Triggers: Code triggers a build; Uses: Tasks use resources; The system implements dedicated conversion rules for each data source, such as JIRA task data conversion rules: Field mapping: JIRA task ID → Unified model task ID; Status conversion: JIRA status "InProgress" → Unified model status "In progress"; Relationship mapping: JIRA task assignment relationship → Unified model "Responsible" relationship; The enterprise has implemented an incremental data flow conversion algorithm. The following is an example of handling changes to development tasks in an actual case: Data change detection: The system detects changes to task "TASK-1024" in JIRA, including the status changing from "To be developed" to "In progress" and the assignee changing from team member A to team member B; Change set calculation: The system calculates the change set : "Change set = { Updated items: {Field: "status", old value: "To be developed", new value: "In progress"}, {Field: "assignee", old value: "Member A", new value: "Member B"} }" Change classification: The system classifies the change set as updated data (no new additions or deletions); Differential transformation application: For status changes and assignee changes, the system only applies transformation functions to the changed fields and does not process unchanged fields such as descriptions and priorities: "Differential transformation result = { Entity update: { Entity type: "Task",​ Entity ID: "UNIFIED-TASK-1024", Updated attributes: { "Current Status": "In Progress", "Status Change Time": "2023-10-16T09:30:45" } } Relationship updates: { Deleted relationship: { Type: "Responsible", Source node: "UNIFIED-MEMBER-A", Target node: "UNIFIED-TASK-1024" } Newly added relationship: { Type: "Responsible", Source node: "UNIFIED-MEMBER-B", Target node: "UNIFIED-TASK-1024" } } }} Data model merging: The system merges the conversion results into the unified data model, updating task node attributes and related relationships; In actual operation, the system adopts a batch processing optimization strategy for frequent changes. For example, during code review, developers may update the same code file multiple times within a short period. The system sets a 5-second change batch window and merges multiple updates to the same file during this period into one processing.

[0127] The enterprise has also implemented a declarative transformation rule engine, enabling business personnel to define and modify transformation rules through a visual interface without writing code. The rule engine supports the following functions: Rule template library: Pre-defined rule templates for common data conversion scenarios, such as task status mapping, personnel role mapping, etc.; Rule verification: Automatically verifies the integrity and consistency of rules and discovers potential conflicts; Rule version management: Tracks the rule change history and supports rolling back to previous versions; Rule learning: Automatically recommends rule optimization suggestions based on historical data; In addition, the enterprise has established a data quality assessment mechanism to monitor data quality issues in the conversion process in real time and trigger automatic repair. For example, when it is detected that a team member associated with a task does not exist, the system will automatically mark the data as "to be verified" and generate repair suggestions. The system also maintains a detailed log of the conversion process, recording the input, output, and quality metrics of each conversion, to support problem location and quality optimization.

[0128] Implementation of business process-aware data flow processing technology: The enterprise has built a business process monitoring system to track the execution and changes of the project management process in real time. The system includes the following core components: Log collector: A lightweight proxy program deployed in each business system that captures the following key activity data: User operation log: Records the operation sequence of users in the project management system; System event log: Records the state changes and process progress automatically triggered by the system; API call log: Records the interface call situations between systems; Database transaction log: Records the changes of business data; Log collection adopts a low-invasive design and is implemented through interceptors, listeners, and log parsers, with an impact on the performance of the original system of less than 3%.

[0129] Process recognition engine: Applies an improved Alpha++ algorithm to extract business process models from activity logs. The algorithm includes the following steps: Log preprocessing: Cleans abnormal data and converts raw events into standard activities; Relationship discovery: Calculates the causal and parallel relationships between activities; Control flow construction: Constructs a process model including sequential, selection, parallel, and loop structures; Decision point identification: Detects key decision points and decision rules in the process; The process recognition engine performs a full analysis every 24 hours and an incremental analysis every 4 hours to maintain the latest process model.

[0130] Change detector: Detects business process changes by comparing process models at different time points. The following case shows the detected changes in the product release approval process: Original process path: Product manager submits a release application → Technical manager approves → Quality manager approves → Department director approves → Release; New process path: Product manager submits a release application → (Technical manager approves, Quality manager approves) [in parallel] → Release committee approves → Release; The change detection algorithm has identified the following change points: New node: Approval by the Release Committee; Deleted node: Approval by the Department Director; Path change: The approval by the Technical Manager and the Quality Manager has changed from serial to parallel; Decision rule change: The approval passing condition has changed from "all approvers pass" to "majority of the committee passes"; The enterprise has established a mapping relationship model between business processes and data flow processing, and defined the functions for the specific implementation rules: Routing rule mapping: Map the decision points and conditional branches in the process to data routing rules; Example: In the release approval process, the "Approval Passed / Rejected" decision point is mapped to the data flow routing condition; IF Approval Result == "Passed" THEN Route to the "Release Preparation" process; ELSE Route to the "Release Cancelled" process; Transformation rule mapping: Map the activity nodes in the process to data transformation rules; Example: The "Quality Inspection" activity is mapped to the quality data aggregation transformation rule; "Aggregation Instruction = { Input: ["Unit Test Results", "Integration Test Results", "Performance Test Results"], Operation: "Calculate Weighted Average Score", Weights: [0.3, 0.4, 0.3], Output: "Comprehensive Quality Score" }" Priority rule mapping: Map the activity priorities in the process to data processing priorities; Example: The emergency bug fix process is mapped to a high-priority data processing flag; IF Task Tag contains "Emergency Fix" THEN Data Processing Priority = "Highest"; When the system detects a business process change, it automatically triggers the data flow reconfiguration mechanism: Change impact analysis: Identify the data flow processing logic affected by the process change. In the case of the release approval process change, the system has identified the following impact points: The data aggregation logic for approval status needs to change from serial to parallel; Data collection and processing related to the "Release Committee" need to be added; The approval decision condition has changed from "all pass" to "majority pass"; Configuration update plan: The system generates a configuration update sequence, including: Preparation stage: Create a new data flow configuration, but do not activate it temporarily; Transition stage: The old and new configurations run in parallel to process in-transit data; Switching stage: Completely switch to the new configuration; Execute configuration update: The system executes the data flow reconfiguration as planned, monitors the system status simultaneously, and automatically rolls back when there are 3 exceptions at a certain node within 10 minutes.

[0131] The enterprise has also developed a process data correlation visualization tool to provide managers with an intuitive view to show the correlation between business processes and data flow processing. This tool supports the following functions: Process visualization: Display the current business process model, including activity nodes, transfer paths, and decision points; Data flow visualization: Display the data flow processing process, including data sources, transformation steps, and target outputs; Correlation mapping visualization: Highlight the mapping relationship between process nodes and data processing steps; Historical comparison: Support viewing historical process versions and change histories; Impact analysis: For planned process changes, preview their potential impacts on data processing; In practical applications, the data flow processing technology with business process awareness enables the system to quickly adapt to business changes. Taking the adjustment of the product development process as an example, when the enterprise switches from the waterfall development model to the agile development model, the system automatically identifies the process changes and accordingly adjusts the data processing logic, including changing from stage-based data aggregation to iterative cycle data aggregation, from fixed milestone reports to continuous integration status reports, etc. This keeps data processing always consistent with actual business needs and avoids the problem of data processing disconnection during business changes in traditional systems.

[0132] Application of the process data synchronization protocol: To ensure data processing consistency during business process changes, the enterprise has implemented a process data synchronization protocol based on distributed consensus. This protocol coordinates data processing among multiple R & D centers globally to ensure the consistency of system states during the parallel operation of the old and new processes.

[0133] Implementation of the process data consistency model: The enterprise has defined and implemented a consistency model , ensuring that at any time point t, each node n in the system has an equivalent view of the business process state and data flow processing state. The specific implementation includes: Hierarchical state synchronization mechanism: Bottom layer: Use a vector clock to track the state updates of each node; Middle layer: Uses Event Sourcing to record state change events; Upper layer: Implements visual Figure 1 Consistency check to ensure that the data processing status is synchronized with the business process status; Consistency check algorithm: The system performs a consistency check every 30 seconds.

[0134] Application of distributed consensus algorithm: The enterprise has implemented a distributed consensus algorithm based on an improved two-phase commit protocol to coordinate the reconfiguration of data streams during business process changes. The algorithm has deployed coordinator nodes in 6 R & D centers globally to ensure the consistency of configuration changes. In the case of a product release process change, the algorithm execution process is as follows: First phase (pre-commit): The main coordinator node sends a pre-commit message containing the change description to all participating nodes; Each node verifies the feasibility of the change and tests the stability of the new configuration; The node returns a ready or reject response to the coordinator, along with detailed verification results; Second phase (commit): The main coordinator node collects the responses from all participating nodes; When more than 80% of the nodes return ready, send a commit message; Otherwise, send an abort message and record the reason for failure; Each node executes or abandons the change according to the coordinator's decision; Pre-verification optimization: Before officially starting the two-phase commit, the system first performs pre-verification of the configuration change, including: Configuration syntax verification: Check the syntax correctness of the new configuration; Conflict detection: Identify potential conflicts with the existing configuration; Performance impact assessment: Predict the impact of the change on system performance; Business process version management system: The enterprise has built a business process version management system to support the parallel operation of new and old business processes: Version library implementation: Uses a graph database to store process models of different versions, supporting: Version tree structure: Records the derivation relationship between process versions; Difference comparison: Highlights the changes between different versions; Tag management: Adds semantic tags to important versions; Version router implementation: Based on a context-aware rule engine, dynamically selects the processing version according to the following factors: Data source: Selects the appropriate version according to the data source system; Project Phases: Different phases of the project may use different process versions; Explicit Marking: Support data contains explicit version markings; Compatibility Checker: Evaluate version compatibility through static analysis and dynamic testing: Static Analysis: Check the compatibility of data structures and flow paths; Dynamic Testing: Use historical data to verify data exchange between different versions; Conflict Resolution: Provide automatic and manual conflict resolution mechanisms; Process Data Switching Manager: During the enterprise's transition from waterfall development to agile development, the switching manager performs the following operations: Data Migration Planning: Establish data mapping relationships between waterfall and agile models: "Example of mapping rules = { "Milestone" → "Sprint", "Phase Delivery" → "Iterative Increment", "Phase Review" → "Iterative Retrospective" }" Status Synchronization Control: During the 6-week transition period, the system maintains the project status of both waterfall and agile models simultaneously: For newly created projects: Directly adopt the agile model; For ongoing projects: Decide whether to switch based on the completion percentage; Completion percentage < 30%: Switch to the agile model; Completion percentage > 70%: Maintain the waterfall model; Completion percentage 30% to 70%: The team can choose autonomously; Rollback Mechanism: To ensure business continuity, the system implements a three-level rollback mechanism: Configuration-level Rollback: Only roll back the data flow configuration and retain the data status; Status-level Rollback: Roll back to the system status at a specified time point; Full Rollback: Restore to the complete system status before the change; Switching Monitoring: Real-time monitor the switching progress and system status: Data Consistency Metrics: Monitor the consistency degree of old and new process data; Performance Metrics: Monitor the system response time and resource utilization during the switching process; User Experience Metrics: Track the user operation error rate and satisfaction; In an agile transformation project, multiple product lines of an enterprise need to switch development modes at different time points. The process data synchronization protocol ensures the continuity and consistency of project data during the transformation process. For example, during the sprint planning phase of a certain product, the system can seamlessly map the requirement planning data in the old process to the sprint plan data in the new process while maintaining the integrity and relevance of historical data.

[0135] The key value of this protocol is that it enables the system to maintain the continuity of data processing during business process changes without downtime or data freezing, significantly reducing the impact of business changes on operations. In practical applications, this protocol ensures that during the 6-month agile transformation period of the enterprise, the project management system always provides accurate and consistent data support, and no data inconsistency problems occur due to process changes.

[0136] Technical effect verification: To verify the technical effects of the method of this application, the enterprise conducted system performance and effect tests after implementation, focusing on evaluating two key technical effects: improved real-time performance and enhanced business adaptability.

[0137] Improved real-time performance of data processing: Based on the actual operation data of 5 major product line projects, the enterprise compared the data processing latency of the system before and after transformation. The following is the comparison of processing latency for different types of data sources: Processing latency of fast-flow data (system monitoring data): Before transformation: average latency 3 minutes, peak latency up to 15 minutes; After transformation: average latency 0.2 seconds, peak latency not exceeding 1.5 seconds; Improvement effect: processing latency reduced by 99.8%, meeting the requirements of real-time monitoring; Processing latency of medium-flow data (task and code submission data): Before transformation: average latency 2.5 hours, peak latency up to 8 hours; After transformation: average latency 45 seconds, peak latency not exceeding 3 minutes; Improvement effect: processing latency reduced by 97%, reaching the quasi-real-time processing level; Processing latency of slow-flow data (financial and personnel evaluation data): Before transformation: average latency 1.5 days, some data latency exceeding 3 days; After transformation: average latency 4 hours, maximum latency not exceeding 12 hours; Improvement effect: processing latency reduced by 89%, significantly improving data timeliness; In the real-time performance test of a specific scenario, the enterprise selected the key business scenario of "code submission triggers the build process" and compared the end-to-end response time before and after transformation: Before transformation: On average, it took 28 minutes from code submission to the availability of data analysis results; After transformation: On average, it only takes 65 seconds from code submission to the availability of data analysis results; Improved effect: The end-to-end response time is reduced by 96%; The improvement of real-time data processing capabilities enables enterprises to achieve the following business values: Early problem detection: The system can issue a warning within an average of 5 minutes after a problem occurs, while it took 3 to 4 hours to detect problems before transformation. This has shortened the average problem repair time by 63%.

[0138] Improved resource utilization: By understanding the resource usage situation in real time, enterprises have optimized resource allocation, and the resource utilization rate has increased from an average of 62% before transformation to 78%.

[0139] Faster decision-making: The time for management to obtain project status reports has been improved from once a day to available at any time, and the decision-making cycle has been shortened from an average of 2 days to 4 hours.

[0140] Enhanced business adaptability effect: To verify the improvement of the business process change adaptation ability, the enterprise recorded multiple business process changes that occurred within 6 months and measured the time and resources required for the system to adapt to these changes. The following is the adaptability comparison of three typical business process changes: Approval process change (from serial approval to parallel approval): Before transformation: It required 3 developers to work for 5 days for system adjustment, and data processing was suspended during the change; After transformation: The system automatically recognizes the process change and reconfigures it. Only 1 administrator needs to confirm, and there is no need to suspend data processing; Improved effect: The labor cost is reduced by 93%, and the business continuity is improved by 100%; Project management method change (from waterfall to agile): Before transformation: It required a special team to work for 3 weeks for system refactoring and manual migration of historical data at the same time; After transformation: The system automatically adapts to the process change, performs data mapping and smooth transition, with a total time consumption of 5 days; Improved effect: The adaptation time is reduced by 76%, and the data consistency error is reduced by 95%; Organizational structure adjustment (department merger and split): Before transformation: It was necessary to manually adjust the data processing logic and permission settings, with an average time consumption of 12 days; After transformation: The system automatically adjusts the data flow processing and permission mapping according to the organizational change, with a time consumption of 2 days; Enhancement effect: The adaptation time is reduced by 83% and the configuration errors are reduced by 89%. During the high-frequency period of business changes (the annual organizational adjustment period), the enterprise measured the consistency between data processing and the actual business processes: Before transformation: After business changes, on average, 42% of the data processing logics were inconsistent with the actual business, and the average correction cycle was 14 days. After transformation: After business changes, the inconsistent ratio dropped to 6%, and the system could automatically correct 95% of the inconsistencies within 24 hours. The improvement in business adaptability has brought significant business value to the enterprise: Reduction in change costs: The implementation cost of process changes is reduced by an average of 85%, from an average of 42 person-days per change to 6.3 person-days.

[0141] Improvement in business agility: The cycle from when the business department proposes a process change to its full implementation is shortened from an average of 25 days to 4 days, enabling the enterprise to respond more quickly to market changes.

[0142] Improvement in user satisfaction: The user satisfaction of the project management system has increased from 68% before transformation to 92%, and the problems of "the system does not meet work requirements" reported by users have decreased by 78%.

[0143] Based on the above verification results, the technical solution proposed in this application has been fully verified in the actual enterprise environment, achieving significant improvements in real-time performance and business adaptability, solving the key technical problems faced by the integration of multi-source heterogeneous data, and providing strong technical support for enterprise project management.

[0144] As Figure 2 and Figure 3 shown, they respectively show the changes in system resource utilization with the increase in data flow and the relationship between the adaptation time of business process changes and the change complexity.

[0145] The above describes the embodiments of the present invention. However, these embodiments are not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative and not restrictive. Under the inspiration of this embodiment, those of ordinary skill in the art can also make more equivalent embodiments in various forms, all of which fall within the protection scope of this embodiment.

Claims

1. An enterprise project management method based on the integration of multi-source heterogeneous data, characterized in that, It includes the following steps: Receiving heterogeneous data from multiple data sources, where the heterogeneous data includes data with different structures, formats, and sources; Adapting the classification of the heterogeneous data according to the update frequency and data importance to form a multi-dimensional heterogeneous data stream with differentiated processing priorities; Assigning a logical timestamp based on business semantics to the multi-dimensional heterogeneous data stream to construct a time-series consistency framework; Performing intelligent transformation, context-aware aggregation, and predictive analysis on the multi-dimensional heterogeneous data stream based on the logical timestamp, and ensuring the logical integrity across data sources according to the dynamically evolving data stream processing model; Applying the analysis results to the multi-level decision support system of enterprise project management through a visual decision matrix.

2. The enterprise project management method based on multi-source heterogeneous data integration according to claim 1, wherein, It also includes: Real-time monitoring of the business process changes in enterprise project management; Automatically perceiving the change patterns and impact scopes of business processes through machine learning algorithms; According to the changes in business processes, adaptively reconstructing the data stream processing logic to achieve the synchronous evolution of the processing logic and business changes.

3. A method for enterprise project management based on multi-source heterogeneous data integration according to claim 1, characterized in that, It also includes: Deploying a lightweight data acquisition adapter with self-learning ability to interface with various data sources and extract data, and the lightweight data acquisition adapter can automatically identify data structure changes and perform adaptation adjustments.

4. A method for enterprise project management based on multi-source heterogeneous data integration according to claim 1, characterized in that, The heterogeneous data includes at least one of relational database data, non-relational database data, document data, log data, API interface data, Internet of Things device data, and social media data.

5. A method for enterprise project management based on multi-source heterogeneous data integration according to claim 1, characterized in that, Executing on an elastic and scalable distributed server cluster, which includes edge data acquisition servers, centralized data processing servers, and intelligent application servers, and can automatically adjust the computing resource allocation according to the data processing load.

6. The enterprise project management method based on multi-source heterogeneous data integration according to claim 1, characterized in that, Built based on an event-driven distributed stream processing framework, and the distributed stream processing framework includes a data stream engine with a fault tolerance mechanism, which can ensure the consistency and integrity of data processing in case of node failures.

7. A method for enterprise project management based on multi-source heterogeneous data integration according to claim 1, characterized in that, The data sources include at least one of enterprise internal project management systems, code management systems, continuous integration and deployment tools, monitoring and logging systems, communication and collaboration tools, document management systems, enterprise resource planning systems, and external market data systems.

8. A method for enterprise project management based on multi-source heterogeneous data integration according to claim 1, characterized in that The assignment of the logical timestamp adopts a multi-level priority algorithm, and dynamically assigns priorities by comprehensively considering the business criticality, update frequency, data dependency relationships, and historical processing patterns of the data stream.

9. The enterprise project management method based on multi-source heterogeneous data integration according to claim 1, characterized in that It also includes: Implementing a differentiated processing strategy for the multi-dimensional heterogeneous data stream, achieving millisecond-level response for critical business data, and at the same time providing a resource-efficient batch processing mechanism for non-critical data.

10. An enterprise project management system based on multi-source heterogeneous data integration, which is used to execute an enterprise project management method based on multi-source heterogeneous data integration according to any one of claims 1-9, characterized in that, It includes: An intelligent data receiving module for receiving heterogeneous data from multiple data sources, where the heterogeneous data includes data with different structures, formats, and sources; An adaptive data classification module for adaptively classifying the heterogeneous data according to the update frequency and data importance to form a multi-dimensional heterogeneous data stream with differentiated processing priorities; A semantic timestamp assignment module for assigning a logical timestamp based on business semantics to the multi-dimensional heterogeneous data stream to construct a time-series consistency framework; The intelligent analysis and processing module performs intelligent conversion, context-aware aggregation, and predictive analysis on multi-dimensional non-uniform data streams based on logical timestamps, and ensures logical integrity across data sources according to the dynamically evolving data stream processing model; The multi-level decision support module is used to apply the analysis results to the multi-level decision support system of enterprise project management through a visual decision matrix.

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