A data-driven enterprise end business process self-adaptive optimization method and system

Abstract

The application discloses a kind of based on data-driven enterprise end business process self-adapting optimization method and system, the present application is first comprehensive acquisition process execution, cross-departmental collaboration, scene characteristics, resource scheduling, customer feedback and system state etc. Whole-link business data, recognize process bottleneck by feature extraction algorithm, according to scene trigger optimization mode switching and generate basic data set.Second, data classification and construct multidimensional associated node, aggregate features form bidirectional mapping optimization associated graph, generate calibration feature vector by multidimensional calibration model.The vector is imported into optimization engine, combined with static and dynamic configuration, scene calibration and artificial feedback, build adaptive optimization base model.Finally, based on real-time data and deviation signal dynamic optimization, balance accuracy and efficiency, output adaptive complex scene, standardized and personalized combined process optimization scheme, realize enterprise business process intelligent, stable, efficient self-adapting iteration.

Description

Technical Field

[0001] This invention relates to the field of multimodal data processing technology, and in particular to a data-driven adaptive optimization method and system for enterprise business processes. Background Technology

[0002] As enterprises deepen their digital transformation, business processes are characterized by multiple scenarios, cross-departmental collaboration, high collaboration, and strong compliance. In particular, in enterprise-level businesses such as customized software development, information system integration, project bidding, procurement and construction, and operation and maintenance services, the process links are complex, the scenarios are significantly different, and the compliance requirements are strict. The traditional fixed process control model is no longer able to adapt to the dynamically changing business needs.

[0003] Existing process optimization methods largely rely on manual review, static configuration, and post-event rectification, resulting in the following technical shortcomings: Incomplete process data collection, lacking real-time collection and dynamic perception of the entire execution status, cross-departmental collaboration, resource consumption, and compliance deviations, making it impossible to accurately identify efficiency bottlenecks and compliance risks. Process optimization depends on human experience, lacking a data-driven adaptive mechanism, making it difficult to adjust and dynamically calibrate in real-time according to changes in scenarios and fluctuations in nodes. Optimization logic is fragmented, failing to form a unified correlation model between core and supporting businesses, resulting in poor cross-module and cross-departmental process collaboration and a tendency for gaps in connection and rule conflicts. There is a lack of a process output mechanism that combines standardization and personalization; unified processes cannot adapt to complex scenarios such as high compliance, large projects, and peak periods, and customized adjustments are costly and have poor reusability. The absence of a closed-loop iteration mechanism means that optimization results cannot be fed back in real-time and continuously iterated, making it difficult to simultaneously balance process optimization accuracy and execution efficiency. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A data-driven adaptive optimization method for enterprise business processes includes: collecting full-chain business data, covering process node execution data, cross-departmental collaborative interaction data, business scenario characteristic data, resource scheduling and configuration data, customer feedback interaction data, and system operation status data; parsing the business data based on a data-driven process feature extraction algorithm, identifying process bottleneck attributes by combining efficiency thresholds and compliance indicators, triggering optimization mode switching through scenario-process adaptation rules, and generating a scenario-adapted basic dataset of business processes; classifying the basic dataset according to core business and supporting business, constructing multi-dimensional process association nodes for each category, and aggregating process optimization features through a cross-module data association algorithm to generate a bidirectional business process-oriented dataset. The process involves mapping and optimizing the correlation graph; modeling based on a multi-dimensional process verification model to uncover the logical consistency relationships of process optimization under various business scenarios, generating process optimization verification feature vectors; importing these feature vectors into the business process adaptive optimization engine, adopting a static process standard configuration mechanism, introducing a dynamic scenario adaptation calibration module, and combining a human experience feedback synchronization mechanism to generate a process optimization update link, forming a basic model for business process adaptive optimization that integrates static and dynamic adaptation and cross-module collaboration; and dynamically optimizing based on the basic model in conjunction with real-time business data streams and process execution deviation correction signals, balancing optimization accuracy and execution efficiency through cross-scenario correlation optimization algorithms to generate standardized and personalized business process optimization solutions that adapt to complex business scenarios.

[0005] A data-driven adaptive optimization system for enterprise business processes is provided. The system is used to execute executable instructions to implement the aforementioned data-driven adaptive optimization method for enterprise business processes.

[0006] Its beneficial effects are as follows: This application achieves dynamic identification of process bottlenecks by collecting multi-source data in real time across the entire chain and combining efficiency thresholds and compliance indicators; it constructs a bidirectional mapping optimization association graph according to core business and supporting business, and generates standardized feature vectors through a multi-dimensional verification model; it builds an adaptive optimization basic model based on the integration of static and dynamic configuration, scenario calibration and human feedback; it completes adaptation calculation, deviation calibration, weight configuration and optimal process selection based on real-time data and deviation signals, forming a closed-loop iterative mechanism, and finally outputs an adaptive process optimization scheme for complex scenarios that takes into account both standardization and personalization.

[0007] This application achieves automatic bottleneck location across four categories—efficiency, compliance, collaboration, and resources—through comprehensive data collection and precise bottleneck identification, improving the accuracy and timeliness of process diagnosis. A two-way mapping relationship graph and multi-dimensional verification model unify process logic and optimization rules, significantly reducing cross-departmental and cross-scenario process conflicts. A static-dynamic fusion adaptive model can automatically switch optimization strategies according to the scenario, balancing standardization with personalized adaptation. Closed-loop iteration and real-time calibration simultaneously improve process cycle time, resource utilization, and compliance rates, reducing collaboration costs and manual intervention. Full-process automated execution results in faster and more comprehensive optimization responses, significantly improving the stability and control capabilities of complex project delivery. Attached Figure Description

[0008] Figure 1 A flowchart of a data-driven adaptive optimization method for enterprise business processes provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a data-driven adaptive optimization system for enterprise business processes, provided as an embodiment of the present invention. Detailed Implementation

[0009] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention. In one embodiment, this application also proposes a data-driven adaptive optimization method for enterprise-side business processes.

[0010] In this application embodiment, a data-driven adaptive optimization method for enterprise-side business processes is provided, such as... Figure 1 As shown: S101 collects full-chain business data of enterprises, covering process node execution data, cross-departmental collaborative interaction data, business scenario characteristic data, resource scheduling and configuration data, customer feedback interaction data, and system operation status data.

[0011] In one implementation, actual operational information of each node in the business process is collected, including node start and completion time, execution status, processing time, completion status, and exception records. For example, in an order processing flow, this includes the execution time, completion rate, and number of exception returns for each step: order placement, review, order picking, shipment, and receipt. Data related to inter-departmental process flow, information transmission, and collaborative processing is also collected, including the number of transfers, response time, interaction results, collaboration efficiency, and connection deviations. For example, records of collaborative behaviors such as document transfer, approval interactions, and information verification between sales, warehousing, and finance departments.

[0012] Collect attribute information about the business scenario, including business type, business scale, time period characteristics, customer type, and scenario complexity. Examples include daily business scenarios, peak promotional periods, dedicated services for major clients, and integrated online and offline business scenarios. Collect data on resource allocation and usage that the process relies on, including personnel configuration, material allocation, system permissions, equipment usage, and budget limits. Examples include the responsible personnel for each process step, the number of available devices, system resource quotas, and material allocation status.

[0013] Collect customer-related feedback, including evaluations, inquiries, complaints, and suggestions, reflecting the impact of processes on customer experience. This includes customer feedback on process timeliness, service quality, and ease of use, as well as records of complaints and improvement requests. Collect operational data from information systems supporting business operations, including system response speed, interface status, data synchronization status, concurrent processing capabilities, and operational stability. This includes business system response time, data transmission latency, system error frequency, and concurrent load. Connect to existing enterprise business systems via a unified interface, synchronizing data according to fixed periods and real-time triggers, outputting timestamp-aligned, formatted, full-dimensional business data directly for subsequent process feature analysis.

[0014] S102 uses a data-driven process feature extraction algorithm to analyze business data, combines efficiency thresholds and compliance indicators to identify process bottleneck attributes, and triggers optimization mode switching through scenario-process adaptation rules to generate a basic dataset of business processes adapted to the scenario.

[0015] In one implementation, the system integrates end-to-end business data collection results with project-wide node status monitoring data. This encompasses the entire business process, including government-enterprise customized software development, information system integration, project bidding, hardware and software procurement, on-site construction, implementation and maintenance, and qualification and compliance management. Through dual verification using efficiency threshold judgment rules and compliance indicator analysis mechanisms, the system dynamically identifies bottleneck characteristics in the operational data of various business processes. Ultimately, it outputs structured, categorizable, and traceable process bottleneck attribute classification results, providing accurate data for subsequent optimization model matching. Specifically, the efficiency threshold judgment is achieved using preset quantitative indicators. Quantitative thresholds are set for each stage of the project, such as standard working hours, delivery cycle, node response time, system resource usage limits, and manpower load limits. The actual process execution data is compared with these thresholds in real time; any indicator exceeding the preset threshold is identified as an efficiency bottleneck. The compliance indicator analysis is conducted based on national laws and regulations, industry regulatory requirements, and corporate internal control systems, including the Bidding Law, Data Security Law, Cybersecurity Law, Level Protection 2.0 requirements, industry qualification licensing standards, government and enterprise customer acceptance standards, and project delivery compliance clauses. Any violations, omissions, delays, or incomplete record-keeping during the process are identified as compliance bottlenecks.

[0016] Taking the target company's core business as an example, the covered processes include the entire process of customized software development for government and enterprise (requirements research - solution design - development coding - testing - deployment and launch - acceptance and payment), the entire process of information system integration projects (solution quotation - equipment procurement - on-site construction - integration and debugging - acceptance), project bidding process, software and hardware procurement process, project implementation and operation and maintenance service process, and enterprise qualification and compliance management log process, etc. The time consumption, workflow status, resource usage, and compliance records of the above processes are collected and identified in all dimensions. In the efficiency threshold determination stage, the specific identification results are as follows: the standard cycle for the requirements confirmation stage of government and enterprise projects is 5 working days, while the actual average execution time is 12 working days, significantly exceeding the threshold and identified as an efficiency bottleneck; the standard delivery cycle for the software and hardware procurement stage is 7 days, while the actual average delivery cycle is 18 days, exceeding the cycle threshold and identified as an efficiency bottleneck; the standard cycle for the on-site construction stage of information system integration projects is 10 days, while the actual average time is 22 days, far exceeding the standard construction period and identified as an efficiency bottleneck.

[0017] In the compliance indicator analysis phase, the specific identification results are as follows: Some software development and system integration projects were deployed online ahead of schedule without completing the required cybersecurity level protection assessment, violating cybersecurity level protection compliance requirements and were identified as compliance bottlenecks; the bidding process had issues such as delayed submission of qualification materials, incomplete review process records, and incomplete process traceability, which did not comply with the Bidding Law and enterprise management standards and were identified as compliance bottlenecks; during the execution of government and enterprise projects, some business data was not anonymized and encrypted as required by the client, violating data security management standards and were identified as compliance bottlenecks.

[0018] Based on comprehensive efficiency assessment, compliance analysis, collaboration status, and resource utilization, four categories of process bottleneck attributes were ultimately identified: Efficiency bottlenecks: Delayed requirement confirmation, excessively long hardware and software procurement cycles, delayed on-site construction and delivery, and low efficiency in process node execution; Compliance bottlenecks: Missing graded protection assessment processes, incomplete documentation of bidding documents and review processes, un-anonymized project data storage, and non-compliant use of qualifications; Collaboration bottlenecks: Disruptions in collaboration among sales, R&D, delivery, and procurement departments, untimely information transmission, delayed customer response, and significant deviations in cross-departmental process coordination; Resource bottlenecks: Insufficient R&D personnel, excessive manpower load, hardware equipment shortages, scheduling conflicts among construction personnel, and excessive system resource usage.

[0019] The above-mentioned process bottleneck attribute classification results comprehensively cover four dimensions: timeliness, compliance, collaboration, and resources. They can accurately reflect the shortcomings and bottlenecks of Longchuang Future in the entire business process of government and enterprise project delivery, system integration, procurement and construction, and operation and maintenance services. This provides complete, accurate, and reliable data support for subsequent optimization of model matching and triggering adaptive adjustments to processes.

[0020] By combining business scenario optimization and adaptation requirements with enterprise process control standards, the results of process bottleneck attribute classification are processed to complete the matching of optimization modes for core business processes and supporting business processes, and generate optimization mode switching trigger signals. In the process, enterprise business processes are first divided into core business processes and supporting business processes based on business revenue contribution, customer delivery attributes, and process control levels. Then, based on different types of efficiency bottlenecks, compliance bottlenecks, collaboration bottlenecks, and resource bottlenecks, corresponding optimization operation modes are matched. At the same time, strict adherence to internal enterprise process control standards, industry regulatory requirements, customer delivery standards, and scenario adaptation rules ensures the rationality, compliance, and feasibility of mode matching. Core business processes are those directly delivered to customers and determine project revenue and the enterprise's core service capabilities. These mainly include customized software development, information system integration, and full-lifecycle project delivery processes. These processes prioritize efficient delivery, strong compliance control, and customer experience, and are matched with corresponding high-priority optimization modes. Supporting business processes are those that ensure the stable operation of core businesses and provide internal management and supporting services. These processes mainly include project bidding, hardware and software procurement, project implementation and maintenance, and qualification and compliance management. The core optimization goals for these processes are process standardization, cost control, and compliance closed-loop traceability, and they are matched with corresponding standardized and lightweight optimization models.

[0021] Taking actual enterprise operation scenarios as an example, the first step is to define business positioning: Customized software development, information system integration, and project acceptance and payment collection are identified as core business processes; processes such as bidding document preparation and submission, software and hardware procurement and supply, project operation and maintenance services, and qualification review and compliance management are identified as supporting business processes. Based on this, bottlenecks and optimization models are matched: For efficiency bottlenecks in core business processes, a project cycle compression + dynamic resource scheduling optimization model is matched, improving overall delivery efficiency by shortening node time, optimizing resource allocation, and adjusting process layout; for compliance bottlenecks in core business processes, a compliance and data security management and pre-acceptance standard optimization model is matched, incorporating compliance verification, security assessment, and acceptance standards into the early stages of the process to avoid rework due to violations later; for efficiency bottlenecks in supporting business processes, a procurement supply chain acceleration and bidding process automation optimization model is matched, reducing time consumption by simplifying approval processes, streamlining supply chain collaboration, and increasing process automation rates; for compliance bottlenecks in supporting business processes, a full lifecycle management of qualifications and full-process traceability optimization model is matched, achieving qualification validity monitoring, process operation record retention, and real-time compliance status verification.

[0022] After completing all optimization mode matching, the system automatically generates a unified optimization mode switching trigger signal. This signal includes the target optimization mode identifier, switching execution conditions, effective scope and priority configuration corresponding to the core business process and the supporting business process, and sends instructions to the business process adaptive optimization engine to trigger the overall switching action of the core business adopting the dual optimization mode of efficient delivery and strong compliance, and the supporting business adopting the optimization mode of process standardization and compliance closed loop, laying the instruction foundation for subsequent signal verification and mode execution.

[0023] Based on business efficiency threshold standards, process node status change information, and optimization mode switching generation rules, the optimization mode switching trigger signals are systematically verified to achieve accurate switching of business process optimization modes in multiple scenarios. The generated optimization mode switching trigger signals are systematically verified through multi-dimensional and multi-level compliance and rationality verification. This ensures accurate switching and stable execution of business process optimization modes in various business scenarios, including government and enterprise project delivery, information system integration, project bidding, hardware and software procurement, on-site construction, and operation and maintenance services.

[0024] This verification mechanism employs a three-tiered progressive verification logic: efficiency threshold compliance verification, node status authenticity verification, and switching rule compliance verification. The system will only execute the optimization mode switching action when all three verifications pass, preventing process optimization failures due to signal anomalies, status distortions, or rule violations, thus ensuring the safety and reliability of adaptive process adjustments across all scenarios. During efficiency threshold compliance verification, the system compares the corresponding process steps in the triggered signal with preset efficiency threshold standards to determine whether key steps such as requirement confirmation, procurement and delivery, on-site construction, and R&D iteration exhibit significant timeouts or inefficiencies that meet the triggering conditions. This confirms the existence of bottlenecks and the fulfillment of optimization initiation conditions, ensuring that optimization triggers are based on objective data.

[0025] During node status verification, the system acquires the current operational status of each process node in real time, including human resource load, resource utilization, supply chain execution status, process progress, and customer delivery node progress. It verifies that the node status upon which the trigger signal is based matches the actual operational status, eliminating invalid switches caused by false or misreported signals, and ensuring that the optimization mode is initiated based on the actual business status. During switch rule compliance verification, the system compares the current mode switch behavior with enterprise process control standards, industry regulatory requirements, data security policies, project delivery compliance clauses, and bidding management regulations to ensure that the switching method, scope of effect, and execution priority of the optimization mode comply with established rules, do not violate compliance bottom lines, do not affect business control order, and do not violate customer delivery constraints.

[0026] After all triple checks pass, the system executes a precise switch to a unified optimization mode across multiple scenarios: core business processes adopt a dual-control optimization mode of efficient delivery and strong compliance, ensuring timely delivery while enhancing security, compliance, and customer experience; supporting business processes adopt a process standardization and compliance closed-loop optimization mode, achieving internal process standardization, cost control, and management traceability. The switch covers all business scenarios, including customized software development, information system integration, hardware and software procurement, project bidding, on-site construction, implementation and maintenance, and qualification compliance management, ensuring that each process link is adjusted synchronously according to a unified optimization strategy, achieving adaptive optimization and collaborative operation of processes across departments, scenarios, and modules.

[0027] Based on the characteristics of the entire business chain and the collaborative interaction relationships between departments, the system automatically matches corresponding optimization dimensions. An optimization model and process attribute compatibility detection mechanism identifies and corrects mismatches, incomplete coverage, and inapplicable rules. This ensures that the optimization model can fully adapt to diverse business scenarios such as software development, information system integration, construction engineering, value-added telecommunications services, and industrial internet data services.

[0028] During the dimension matching optimization phase, the system automatically matches data from a pre-defined dimension library based on process node attributes, business link structure, and departmental collaboration relationships. The matching dimensions include six categories: timeliness optimization, resource optimization, collaboration optimization, compliance optimization, customer experience optimization, and supply chain optimization. Specifically, timeliness optimization addresses efficiency issues such as excessively long process cycles and slow node execution; resource optimization adjusts the allocation of manpower, equipment, materials, and system quotas; collaboration optimization removes barriers to information flow and collaboration across departments, positions, and systems; compliance optimization meets regulatory and internal control requirements such as information security assessments, qualification licenses, data security, and process traceability; customer experience optimization improves response speed, service quality, and delivery satisfaction; and supply chain optimization improves operational efficiency in procurement, supply, inventory, and logistics.

[0029] During the compatibility testing phase, the system compares the matched optimization patterns with the current business type, qualification requirements, customer level, scenario complexity, and control intensity item by item. When situations such as mismatch between the optimization pattern and the business scenario, lack of coverage of key processes, missing compliance rules, conflicting resource scheduling logic, or improper permission configuration occur, the system automatically marks them as matching anomalies and categorizes them according to the anomaly type, including incomplete pattern coverage, scenario adaptation deviation, missing compliance rules, inapplicable resource rules, and lack of collaboration mechanisms.

[0030] In real-world business scenarios, typical business characteristics include processes that span multiple departments and require collaborative execution. Projects are characterized by high compliance, high customization, high collaboration, and high control intensity. Based on these characteristics, precise matching can be implemented: when there are delays in requirement confirmation or low efficiency in coordination, the system automatically matches the customer collaboration and requirement management optimization dimensions; when there are excessively long procurement cycles or untimely delivery, the system automatically matches the supply chain and inventory optimization dimensions; when there are on-site construction delays or chaotic personnel scheduling, the system automatically matches the resource scheduling and construction management optimization dimensions; and when there are data security risks, non-standard use of qualifications, or missing assessment processes, the system automatically matches the graded protection, data security, and qualification control optimization dimensions.

[0031] In the anomaly detection and pattern correction phase, the system can identify instances where the initial optimization pattern fails to cover aspects such as construction permit management, value-added telecommunications service qualification control, and industrial internet data compliance requirements, marking these as incomplete pattern coverage anomalies. Subsequently, by supplementing the construction compliance control submodule, telecommunications service qualification management submodule, and industrial internet data security compliance submodule, and improving process rules, permission verification, timing control, and compliance verification logic, a complete optimization pattern covering all business scenarios, all process stages, and all compliance requirements is formed. This ensures that adaptive process optimization can operate stably, compliantly, and efficiently in various business scenarios.

[0032] Following preset business process data integration rules, the categorized process information is associated and integrated with matching optimization modes and corresponding business source data to generate a scenario-adapted basic business process dataset. During the integration process, based on end-to-end business characteristics and cross-departmental collaborative interactions, optimization dimensions consistent with process bottlenecks and business attributes are automatically matched. An optimization mode and process attribute compatibility detection mechanism is used to mark and correct anomalies such as mode mismatch, incomplete coverage, and rule inapplicability, ensuring that the final dataset and optimization strategies are fully adaptable to diverse business scenarios such as software development, information system integration, construction engineering, value-added telecommunications services, and industrial internet data services.

[0033] During the data integration and association phase, pre-defined business process data integration rules use process nodes as the basic unit and timestamps, business numbers, and scenario identifiers as the association criteria. This process aligns and normalizes process classification information, optimization models, optimization dimensions, and business source data in a structured manner, ensuring consistent data across dimensions such as timeliness, compliance, collaboration, resources, customer experience, and supply chain. Specifically, process information includes the division between core and supporting businesses, process node attributes, and departmental collaboration relationships; optimization models include different types of operating modes such as efficient delivery, strong compliance control, process standardization, cost control, and compliance closed-loop; optimization dimensions include six categories: timeliness optimization, resource optimization, collaboration optimization, compliance optimization, customer experience optimization, and supply chain optimization; and business source data includes process execution records, resource usage data, compliance verification records, customer interaction information, and supply chain operation data.

[0034] In the automatic matching of optimization dimensions, the system achieves precise matching based on the type of process bottleneck and the characteristics of business operation: when problems such as delayed confirmation of requirements or untimely customer communication occur, the system automatically matches the customer collaboration and requirement management optimization dimension; when problems such as excessively long procurement cycles or slow inventory turnover occur, the system automatically matches the supply chain and inventory optimization dimension; when problems such as on-site construction exceeding time limits or chaotic personnel and equipment scheduling occur, the system automatically matches the resource scheduling and construction management optimization dimension; when there are risks such as missing information security assessment, non-standard data management, or non-compliant use of qualifications, the system automatically matches the information security level protection, data security, and qualification control optimization dimension.

[0035] In the compatibility testing and anomaly handling phase, the system compares the optimized model with business type, industry qualification requirements, customer level, scenario complexity, and control intensity item by item. When an optimized model fails to cover construction permit management, value-added telecommunications service qualification management, or industrial internet data compliance requirements, it is automatically marked as an anomaly of incomplete model coverage, and a correction mechanism is initiated. By supplementing sub-modules such as construction compliance management, telecommunications service qualification management, and industrial internet data security compliance, the system improves process rules, permission verification, timing control, and compliance verification logic, forming a complete optimized model covering all business, all processes, and all compliance requirements.

[0036] After completing pattern correction and dimension matching, the system integrates various data according to unified integration rules, ultimately generating a basic dataset for business processes adapted to specific scenarios. This dataset is structured, standardized, and scenario-based, containing both static process configuration information and dynamic operational features and optimization strategy information. It can directly provide complete, accurate, and reliable data support for subsequent bidirectional mapping optimization association graph construction and feature vector generation, ensuring the stable operation of the entire process for adaptive optimization of business processes.

[0037] S103 categorizes the basic dataset into core business and supporting business, constructs multi-dimensional process association nodes for each category, aggregates process optimization features through cross-module data association algorithms, and generates a bidirectional mapping optimization association graph oriented towards business processes.

[0038] In one implementation, the basic business process dataset is processed and categorized into core business and supporting business. Each category includes process nodes, collaborative relationships, resource allocation, scenario characteristics, and efficiency indicators. Dynamic features such as process time consumption, interaction frequency, resource consumption, and compliance deviations are aggregated through a dynamic feature window. Data on process execution, collaborative interaction, and scheduling control for each type of process is extracted to generate corresponding subsets. During the data processing phase, the basic business process dataset adapted to the scenario is first cleaned to remove outliers, missing values, and duplicate records, and to standardize data definitions, timestamps, and formats. Then, the processes are divided into core business and supporting business according to preset classification rules. The classification criteria are as follows: business processes that directly deliver to customers, generate core revenue, and reflect the company's core service capabilities are defined as core businesses; business processes that ensure the stable operation of core businesses and undertake internal management and supporting service functions are defined as supporting businesses.

[0039] During the dynamic feature aggregation phase, a dynamic feature window mechanism is adopted, using a fixed duration or complete process cycle as a sliding unit to collect and calculate features of the entire process operation data in real time, aggregating four types of dynamic features: process time consumption features reflect the execution cycle and timeout of each link; interaction frequency features reflect the frequency of collaboration and response efficiency between departments, positions, and systems; resource usage features reflect the usage load and allocation of resources such as manpower, equipment, systems, and materials; and compliance deviation features reflect the degree of deviation between process execution and compliance standards and control requirements.

[0040] During the data subset generation stage, based on the aggregated dynamic characteristics, three types of data are extracted according to data usage and management dimensions to form exclusive business subsets: process execution subset is used to record the entire process, execution steps, cycle and key node status; collaborative interaction subset is used to record cross-departmental flow, information exchange, collaborative response and connection deviation data; scheduling and control subset is used to record resource allocation, personnel scheduling, equipment scheduling, inventory allocation and qualification usage data.

[0041] In typical business scenarios, core businesses encompass customized software development for government and enterprises, information system integration services, artificial intelligence application system integration, and industrial internet data services; supporting businesses include project bidding, computer hardware and software sales, project implementation and maintenance, qualification and compliance management, construction coordination, and value-added telecommunications service support. Each type of data includes standardized fields: process nodes include requirements gathering, development coding, integration and debugging, acceptance and payment collection, procurement and delivery, and qualification review; collaborative relationships include interaction links between multiple departments such as sales, R&D, delivery, procurement, and maintenance; resource allocation includes R&D personnel, server resources, construction equipment, hardware and software inventory, and qualification quotas; scenario characteristics include daily delivery, large government and enterprise projects, peak construction periods, and compliance reviews; and efficiency indicators include cycle time, on-time delivery rate, procurement response speed, and approval time.

[0042] During dynamic feature aggregation, quantitative results such as process time, interaction frequency, resource consumption, and compliance deviations can be obtained. Based on these features, three business subsets are further generated: the process execution subset includes data such as software development cycle, integration and debugging steps, and acceptance nodes; the collaborative interaction subset includes data such as cross-departmental workflow records, customer interaction logs, and collaboration response times; and the scheduling and control subset includes data such as personnel scheduling, equipment allocation, inventory scheduling, and qualification usage records. Through the above processing, the basic dataset is standardized, contextualized, and structured, providing a stable and reliable data foundation for subsequent construction of related nodes, feature aggregation, and generation of association graphs.

[0043] The system uses core business as the core node, supporting business as the collaborative node, and process optimization as the target node. It generates node attribute information by combining process mapping and scenario recognition, and generates edge association information based on data correlation and optimized response sequence. In terms of node hierarchy construction, a three-tiered architecture is adopted: core nodes, collaborative nodes, and target nodes. Core nodes, as first-level nodes, correspond to core business processes that directly deliver to customers and bear core value output; they are the main body of the entire optimized association graph. Collaborative nodes, as second-level nodes, correspond to supporting business processes that provide support and internal collaboration for core business operations, connecting the necessary supporting links for core business operation. Target nodes, as third-level nodes, correspond to the expected direction and quantitative indicators of process optimization, serving as the guiding unit driving adaptive process adjustments.

[0044] Regarding node attribute generation, each node contains standardized attribute fields, specifically including business type, scenario tag, efficiency baseline, compliance requirements, and resource limit. The business type is used to distinguish the business scope to which the process belongs; the scenario tag is used to identify the current operational scenario of the business; the efficiency baseline is used to record the standard execution time and cycle threshold of the node; the compliance requirements are used to clarify the regulatory norms, internal control clauses, and security standards that the node must meet; and the resource limit is used to limit the maximum amount of human resources, equipment, systems, materials, and other resources that the node is allowed to use.

[0045] In generating edge association information, directed connections between nodes are established based on both data association degree and optimized response sequence. Data association degree reflects the tightness of data interaction and business dependency between two process nodes; optimized response sequence reflects the logical relationship between nodes in terms of execution order and triggering sequence. The edge association rule is as follows: the higher the data association degree between nodes and the closer the optimized response sequence, the larger the edge weight assignment, indicating a stronger coupling relationship between nodes. When any node experiences an anomaly, the other node must respond and adjust synchronously.

[0046] In typical business scenarios, node definitions and attribute generation are as follows: Core nodes correspond to core business processes such as government and enterprise customized software development projects and information system integration projects. Their attributes include high compliance scenario tags, standard cycle targets, and upper limits for R&D and implementation resource allocation. Collaboration nodes correspond to supporting business processes such as bidding management, software and hardware procurement, operation and maintenance services, qualification management, and construction coordination. Their attributes include supply chain assurance tags, standard delivery cycles, and upper limits for personnel and material resources. Target nodes correspond to optimization directions such as cycle compression, compliance closure, resource balancing, and cost reduction. Their attributes include optimization direction, trigger threshold, and execution priority.

[0047] In terms of constructing edge-related information, strong correlation edges are formed between core business and supporting business based on business dependencies. For example, there is a strong data correlation between the software development process and the procurement process, and the procurement process must be completed before the development process can begin, thus forming a high-weight directed edge. After the information system integration and debugging is completed, it needs to be connected to the operation and maintenance process immediately, forming a closely related edge in terms of time sequence. The qualification management process needs to complete compliance verification before the bidding process starts, forming a high-weight forward related edge. Through the structured construction of the above nodes and edges, a logically clear and complete process correlation basic structure is formed.

[0048] A bidirectional directed graph is constructed, comprising core business nodes across the entire process chain, multiple types of supporting business collaboration nodes, and differentiated optimization judgment nodes. A cross-module association enhancement mechanism strengthens the feature coupling strength between processes. Based on a data-driven multi-module coupling model combined with feedforward prediction, feedback calibration, and dynamic adaptation verification modes, corresponding subset data is input to calculate optimization parameter configurations and feature aggregation algorithm parameters. Solution transformation is performed based on business attributes and process characteristics, generating an integrated business process bidirectional mapping optimization association graph. During the graph structure construction phase, core business nodes serve as the main nodes of the entire process, supporting business collaboration nodes as auxiliary association nodes, and differentiated optimization judgment nodes as trigger and adjustment nodes, forming a bidirectional directed structure of forward process execution and reverse optimization control. The forward link represents the complete execution path of the business from initiation to delivery, while the reverse link represents the transmission and adjustment path of the optimization target to each process node, achieving bidirectional interconnection and closed-loop linkage between process execution and optimization control. By strengthening the cross-module association mechanism, the feature coupling of process links with strong dependencies, strong collaboration and strong constraints is strengthened to improve the tightness of association and ensure that when any node has efficiency deviation, compliance abnormality or resource fluctuation, the associated nodes can respond synchronously and trigger adaptation adjustments.

[0049] During the model computation phase, a data-driven, multi-module coupled model is adopted, which includes three operating modes: feedforward prediction, feedback calibration, and dynamic adaptation verification. Feedforward prediction, based on historical process data and current operating conditions, predicts cycle time, resource consumption, and compliance risks in advance; feedback calibration corrects the prediction results based on real-time collected process deviation data; dynamic adaptation verification optimizes the correlation strength and parameter configuration through multi-scenario comparison and iteration, and finally outputs stable and reliable optimized parameters and feature aggregation parameters.

[0050] The model is structured in four layers: input layer, feature coupling layer, computation layer, and output layer. The input layer receives three types of data: process execution subset, collaborative interaction subset, and scheduling and control subset. The feature coupling layer enhances modules through cross-module associations, strengthening dependencies and linkages between processes. The computation layer performs prediction, calibration, and verification calculations based on a data-driven multi-module coupling model. The output layer outputs a complete bidirectional mapping optimization association graph. The layers are connected using a combination of full connectivity and directional connectivity. The input layer and feature coupling layer are fully connected to ensure complete feature transmission; the feature coupling layer and computation layer are directionally connected according to actual business links to align with process logic; and the computation layer and output layer are mapped one-to-one to ensure accurate output.

[0051] During model training, the entire historical project data is first imported to initialize node and edge weights. Then, feedforward prediction is used to forecast process status and risks. Next, real-time data is integrated for feedback calibration to reduce prediction bias. Finally, dynamic adaptation across multiple scenarios is used to adjust the correlation strength and parameters until the model maintains stable output across different scenarios. Key model parameters include: feature coupling strength coefficient (0.8–1.0 for strong correlation, 0.5–0.79 for moderate correlation); feedforward prediction time window can be set according to a fixed period or the entire project lifecycle; feedback calibration trigger thresholds are set for efficiency deviation greater than 10% and compliance deviation greater than 5%.

[0052] In typical business scenarios, the forward path of the bidirectional mapping directed graph sequentially links core business processes with supporting business operations, resource scheduling, and optimization goals. The reverse path, on the other hand, uses optimization goals to drive adjustments to supporting business processes and further refine core business workflows. Through enhanced cross-module connections, the linkage and coupling strength between core development, procurement, and construction stages can be significantly improved, while strengthening the closed-loop connection between qualification management, bidding, and project acceptance. After calculation, the model outputs optimization parameters such as cycle compression, resource allocation, and compliance enhancement, as well as aggregated weights for three types of features: time consumption, compliance, and collaboration. Ultimately, it generates a bidirectional mapping optimization connection graph that intuitively displays the dependencies, bottleneck locations, optimization paths, and triggering conditions throughout the entire process.

[0053] The system compares and analyzes basic process data with historical best-case operating data. Through intelligent linkage of various business modules, online analysis is performed. Based on process efficiency deviations, measured collaborative costs, and scenario adaptation feedback data, multi-dimensional data mapped in real-time by a data-driven model is used for feedback calibration, iteratively optimizing the correlation diagram. In the comparative analysis phase, using historical best-case operating conditions as a benchmark, the current basic process data is compared item by item from multiple dimensions, including cycle time, operating costs, resource utilization, compliance compliance rate, and customer satisfaction. This identifies the gap between actual operation and the optimal state, generating quantitative deviation results to provide objective evidence for subsequent calibration. During online analysis, the system intelligently links all business modules, including R&D, sales, procurement, delivery, operations, and compliance, to monitor and comprehensively assess process execution status, resource scheduling, cross-departmental collaboration effectiveness, and compliance execution in real time. Key indicators such as efficiency deviations, incremental collaborative costs, and scenario adaptation deviations are extracted to identify process bottlenecks and optimization opportunities.

[0054] Feedback calibration is based on real-time deviation signals, scenario adaptation feedback results, and multi-module linkage analysis results. Combined with the real-time mapping relationships output by the data-driven model, it dynamically adjusts the node weights, edge association strengths, optimization trigger thresholds, and feature aggregation parameters in the bidirectional mapping optimization association graph. By strengthening the association relationships of key nodes, implementing pre-compliance verification, optimizing cross-departmental collaboration paths, and increasing resource scheduling priority, it corrects process logic and parameter configurations, making the association graph more aligned with actual business operation patterns. The iteration rules combine project-based closed-loop iteration with periodic iteration. Each completed project process automatically executes an iterative optimization, while global iterative updates are performed at fixed time intervals to ensure the association graph continuously adapts to changes in business, scenarios, and control requirements.

[0055] In typical business scenarios, the historical best performance is characterized by standard timeframes for demand confirmation, procurement and delivery, and on-site construction, resulting in optimal compliance rates. Current process data shows significant deviations in both timeframe and compliance rates across all stages. Online analysis reveals key findings such as deviations in process execution efficiency, increased cross-departmental collaboration costs, and higher customer demands for compliance and timeliness. Based on this, feedback calibration is conducted, adjusting the weights of resource scheduling nodes, moving compliance verification nodes earlier in the process, optimizing information flow paths between departments, and reducing redundant connections. After iterative calibration, an updated bidirectional mapping optimization graph is output, better adapting to complex business scenarios such as high-standard project delivery, peak-period construction, and stringent qualification reviews. This provides high-quality, highly reliable graph structure data for subsequent multi-dimensional process verification models, improving the accuracy and stability of verification feature vector generation.

[0056] At the algorithm and business integration level, input data includes basic process datasets, core and supporting business classification results, dynamic feature window aggregation data, historical best operating condition data, and real-time process deviation data. The dynamic feature aggregation algorithm adapts to various business scenarios, while the cross-module data association algorithm matches the collaborative operation mode across departments and business lines within an enterprise. The data-driven multi-module coupling model, combined with feedforward prediction, feedback calibration, and dynamic adaptation verification logic, aligns with the operational characteristics of project-based full-cycle delivery. The final output is an iteratively optimized bidirectional mapping association graph, containing complete nodes, edges, association weights, optimization parameters, and trigger thresholds. This graph can be directly input into a multi-dimensional verification model, providing reliable support for generating process optimization verification feature vectors.

[0057] S104 is based on a multi-dimensional process verification model to model and mine the logical consistency relationship of process optimization in various business scenarios, and generate process optimization verification feature vectors.

[0058] In one implementation, a bidirectional mapping optimization association graph is used as input. By constructing a multi-dimensional process verification model, consistency verification is performed on the process optimization logic of various business scenarios such as software development, system integration, bidding, procurement, operation and maintenance, and compliance management. The model explores reusable, transferable, and alignable general optimization rules between different scenarios and finally outputs a standardized and highly reliable process optimization verification feature vector, providing stable and unified feature input for the subsequent adaptive optimization engine.

[0059] The model adopts a layered modular architecture, as follows: Input Layer: Used to access the bidirectional mapping optimization association graph, containing complete graph structure data such as node information, edge relationships, weight configuration, optimization parameters, and scene labels. Feature Parsing Layer: Contains a process logic parsing submodule, a scene feature extraction submodule, and a compliance rule matching submodule, which respectively complete process link parsing, scene attribute extraction, and compliance rule verification. Consistency Verification Layer: Contains a logic conflict detection submodule, a cross-scene rule alignment submodule, and a parameter rationality verification submodule, used to identify optimization logic contradictions, complete cross-scene rule unification, and verify the validity of parameter configurations. Output Layer: Outputs a set of process optimization logic consistency relationships and standardized process optimization verification feature vectors. The input layer and feature parsing layer use a fully connected approach to completely transmit all feature information of the association graph. The feature parsing layer and consistency verification layer are directionally connected according to three independent links: process, scene, and compliance, ensuring the relevance of parsing and verification. The consistency verification layer and output layer use a one-to-one mapping to ensure accurate output of verification results and feature vectors.

[0060] The model training process is as follows: First, import all historical project business data, covering all scenarios including software development, system integration, procurement, bidding, operation and maintenance, and compliance management. Second, initialize the validation rule base, including basic rules such as efficiency thresholds, compliance clauses, cross-departmental collaboration standards, and customer acceptance criteria. Third, perform forward validation, checking the process logic, parameter configuration, and scenario adaptability of the optimized relationship graph item by item. Fourth, mark issues such as logical contradictions, parameter out-of-bounds errors, scenario mismatches, and compliance conflicts. Fifth, revise the validation rules and weight coefficients based on feedback from real project execution results. Sixth, conduct convergence validation; when the validation accuracy of multiple consecutive projects reaches a preset threshold, the model training is complete and the model is put into use.

[0061] This multi-dimensional process validation model extracts process optimization logic that is universal across scenarios, unified across businesses, and compatible across departments, forming a reusable, transferable, and alignable consistency relationship. Efficiency logic consistency: All scenarios adhere to a unified logic of controllable cycles, decreasing time consumption, and no redundant waiting. Compliance logic consistency: The entire process must meet requirements for complete information security compliance, complete qualifications, operational traceability, and auditability. Collaboration logic consistency: Cross-departmental processes require information synchronization, clear responsibilities, timely response, and smooth connection. Resource logic consistency: All business resources are not over-allocated, not wasted, dynamically scheduled, and allocated on demand. Customer logic consistency: All delivery stages meet acceptance standards, data security, and stable and controllable user experience. In actual business scenarios, efficiency consistency is reflected in ensuring that critical paths are not delayed and total cycles are not exceeded in software development, system integration, and on-site construction processes; compliance consistency is reflected in ensuring valid qualifications, auditable processes, and data anonymization in bidding, deployment, and acceptance delivery; collaboration consistency is reflected in real-time information synchronization and smooth flow between business-related departments; and resource consistency is reflected in the dynamic adjustment of resources such as manpower, equipment, and inventory on demand. Ultimately, a set of consistent rules for process optimization logic covering all scenarios is formed.

[0062] The process optimization logic consistency relationships, verification results, scenario characteristics, and optimization parameters are uniformly vectorized and encoded to form a fixed-dimensional, standardized verification feature vector that can be directly input into the adaptive optimization engine. The vector mainly includes the following dimensions: Process efficiency characteristics: cycle deviation, time consumption compliance level, and waiting stage ratio. Compliance verification characteristics: completeness of information security assessment, completeness of qualifications, completeness of process traces, and data security compliance. Collaboration characteristics: degree of cross-departmental synchronization, response timeliness, and level of connection deviation. Resource characteristics: manpower load, equipment occupancy, inventory status, and system resource utilization. Scenario characteristics: scenario identifiers such as high-compliance projects, large-scale projects, peak periods, and routine delivery. Logical consistency characteristics: number of logical conflicts, rule alignment degree, and overall compliance rate. Taking a typical software development project as an example, the vector can fully represent process timeliness, compliance status, collaboration level, resource load, scenario type, and logical consistency, ultimately outputting a multi-dimensional process optimization verification feature vector. The data is complete, conflict-free, and uniformly formatted, and can be directly used for building the basic model for subsequent business process adaptive optimization.

[0063] S105 imports the feature vector into the business process adaptive optimization engine, adopts a static process standard configuration mechanism, introduces a dynamic scenario adaptation calibration module, and combines a human experience feedback synchronization mechanism to generate a process optimization update link, forming a basic model for business process adaptive optimization that integrates static and dynamic adaptation and cross-module collaboration.

[0064] In one implementation, the process optimization verification feature vector is combined and matched with business process attribute features. A static process standard configuration algorithm and a cross-module optimization feature aggregation mechanism are introduced to achieve cross-domain mapping and feature fusion for business process optimization. In the combination and matching stage, the process optimization verification feature vector is used as the data foundation and matched item by item with the business process attributes. The feature values ​​at the data level are associated with the process attributes, control requirements, resource conditions, and scenario constraints at the business level to ensure that each optimization feature can be mapped to an actual executable process step.

[0065] During the execution of the static process standard configuration algorithm, a fixed baseline and standard configuration for process execution are generated based on the company's internal control system, industry regulatory standards, project delivery and acceptance standards, and compliance management requirements. This includes standard node timeliness, standard resource quotas, standard flow paths, and standard compliance verification points, providing a unified, stable, and reusable benchmark framework for process optimization. The cross-module optimization feature aggregation mechanism is used to aggregate process features and optimization logic from multiple modules such as R&D design, marketing and sales, procurement and supply, project delivery, implementation and maintenance, and compliance control. This eliminates data barriers and feature silos between modules, unifying and integrating optimization rules, collaborative relationships, constraints, and anomaly strategies scattered across various departments and stages to form a globally callable optimization feature library. Cross-domain mapping is used to map process optimization verification feature vectors from the data dimension to the business process dimension, transforming abstract numerical features into understandable, executable, and schedulable process optimization instructions, enabling the model output to directly interface with business execution systems and process scheduling platforms.

[0066] In typical business scenarios, input data includes process optimization verification feature vectors and business process attributes. These attributes cover all business types, including software development, information system integration, bidding management, hardware and software procurement, on-site construction, and operation and maintenance services. During the combination and matching process, efficiency features correspond to process attributes such as project cycle and delivery timeliness; compliance features correspond to attributes such as graded protection assessment, qualification licensing, data security, and process traceability; collaboration features correspond to attributes such as cross-departmental workflow, customer interaction, and collaborative response; and resource features correspond to attributes such as manpower allocation, server resources, construction equipment, and material inventory.

[0067] To align with enterprise business management standards and static / dynamic scenario optimization requirements, a multi-dimensional process optimization node model is constructed. A cross-domain adaptive precision calculation model is used to calculate the optimization mapping weights of each node, establishing a dynamic optimization association mechanism between business processes to achieve adaptive linkage and precise optimization scheduling between process nodes. During the node model construction phase, based on internal enterprise management standards, industry regulatory requirements, project delivery standards, and dynamic optimization needs under different scenarios, the process is broken down into multiple independent optimization units, forming a multi-dimensional process optimization node model covering the entire process. Each node corresponds to a key step in the process and possesses independent optimization logic, constraints, and triggering rules.

[0068] To achieve precise matching between nodes and business scenarios and optimization goals, a cross-domain adaptation precision calculation model is adopted to quantify the mapping relationship of each optimization node and derive the node optimization weight. The weight reflects the node's optimization contribution, execution priority, and correlation strength in the overall process, providing a quantitative basis for subsequent dynamic adjustments. After completing the weight calculation, a dynamic optimization correlation mechanism is established between process nodes. When any node experiences efficiency deviations, compliance anomalies, resource fluctuations, or collaboration breakdowns, its associated nodes can automatically respond and adjust synchronously according to preset rules, forming a full-process linkage optimization.

[0069] The model consists of the following modules: Input Layer: Access process optimization verification feature vectors, business process attributes, static standard configuration data, and dynamic scenario operation data. Node Construction Layer: Includes process node sub-modules, scenario adaptation sub-modules, compliance control sub-modules, and resource scheduling sub-modules, respectively completing node definition, scenario matching, compliance verification, and resource configuration. Weight Calculation Layer: Employs a cross-domain adaptation precision calculation model to uniformly quantify node mapping relationships, optimization contributions, and scenario matching degrees, outputting node optimization weights. Association Establishment Layer: Establishes directed dynamic associations between nodes based on node weights and optimization density, forming an automatically linked optimization network. The input layer to the node construction layer is connected according to the actual business process links, ensuring that the data flow is consistent with the business execution path. The node construction layer to the weight calculation layer uses a fully connected approach, ensuring that each node feature participates in the weight calculation. The weight calculation layer to the association establishment layer generates association relationships from high to low optimization density, ensuring that strongly linked nodes are linked first.

[0070] The model training process is as follows: First, import historical process standards, multi-scenario operation rules, and historical optimization result data to construct a basic training set. Second, initialize the type, attributes, constraints, and default parameters of each optimization node. Third, run the cross-domain adaptation accuracy calculation model to evaluate the node mapping effect and generate standardized node weights. Fourth, establish dynamic optimization relationships between nodes based on the weights and business dependencies. Finally, validate the model using real project data. When the weight calculation error is below a set threshold, the model converges and is put into use.

[0071] Specifically, based on the entire process, optimization nodes are constructed for requirements gathering, development cycle, procurement timeliness, construction scheduling, compliance with graded protection regulations, and acceptance and payment collection. Each node corresponds to independent optimization goals and control rules. For customized software development: compliance control weight 0.35, execution efficiency weight 0.35, cross-departmental collaboration weight 0.2, resource scheduling weight 0.1. For information system integration: execution efficiency weight 0.4, resource scheduling weight 0.25, compliance control weight 0.2, cross-departmental collaboration weight 0.15. When an anomaly occurs at a requirements node, the timing and resource allocation of development, procurement, and construction nodes are automatically adjusted synchronously. When a compliance node triggers verification, the entire process nodes simultaneously strengthen the enforcement of rules related to graded protection assessment, qualification verification, and data security.

[0072] Using optimized correlation tightness as the core dimension, a multi-dimensional process optimization correlation matrix is ​​constructed by integrating static process configuration data, dynamic scenario calibration data, cross-module collaborative interaction data, and human experience feedback data. This matrix achieves a unified quantitative expression of static standards and dynamic adaptation, cross-departmental collaboration, and human experience. During matrix construction, the optimized correlation tightness between process nodes is used as the core measurement dimension. The four types of heterogeneous data are normalized and feature-fused to form a computable, schedulable, and optimizable multi-dimensional correlation structure. This matrix can comprehensively characterize the correlation strength of process nodes under static constraints, dynamic changes, collaborative interactions, and experience-based decision-making, providing a unified data carrier for subsequent deviation correction and model fusion.

[0073] Static process configuration data includes fixed configuration information such as enterprise standard process paths, control systems, execution thresholds, timeliness baselines, and compliance bottom lines, providing an unbreakable static constraint framework for process optimization. Dynamic scenario calibration data includes real-time scenario status data such as peak business periods, major project implementations, high compliance requirements, and emergency construction scheduling, used to dynamically adjust optimization strategies based on current operating conditions. Cross-module collaborative interaction data covers data such as inter-departmental process flow records, response times, information synchronization frequency, and collaboration connection deviations, used to characterize the smoothness of cross-module and cross-departmental process collaboration. Human experience feedback data integrates experience rules, anomaly handling strategies, and priority judgment criteria formed by project managers, technical leads, compliance specialists, and implementation engineers in long-term business execution, improving the practicality and reliability of optimization solutions.

[0074] The optimization correlation tightness is used to measure the strength of dependency, response speed, and impact of optimization between two process nodes. Higher tightness indicates a higher priority and stronger linkage when one node experiences an anomaly, requiring simultaneous adjustments in the other. In typical business scenarios, there are strong correlations between core business processes and supporting business processes: the process development stage and the procurement and supply stage have a high dependency, resulting in a high optimization correlation tightness; the qualification management process and the bidding process have a pre-constraint relationship, leading to an even higher correlation tightness; the construction implementation process and the operation and maintenance service process have a sequential connection, exhibiting a high correlation tightness.

[0075] The matrix uses process optimization nodes as rows and four types of data—static standards, dynamic scenarios, collaborative data, and human feedback—as columns. The matrix values ​​represent the optimization correlation and weight of corresponding nodes across different dimensions. Through normalized calculation and weighted fusion, the matrix comprehensively reflects the optimization relationships of each process node under combined static and dynamic conditions, cross-departmental collaboration, and human-machine collaboration, achieving a structured and digital expression of the entire process optimization logic. This matrix can directly provide input for subsequent cross-domain optimization deviation correction models, supporting the construction of a foundational model for adaptive business process optimization.

[0076] By addressing optimization gaps through a cross-domain optimization deviation correction model and strengthening static process optimization correlation characteristics through a static process standardization propagation mechanism, this model is integrated with the results of a multi-dimensional process optimization node model to generate a business process adaptive optimization foundation model that integrates static and dynamic adaptation and cross-module collaboration. The cross-domain optimization deviation correction model uniformly handles issues such as process optimization correlation gaps, connection breaks, and logical conflicts. Combined with the static process standardization propagation mechanism, static optimization correlation characteristics such as compliance requirements, timeliness standards, and process baselines are strengthened and transmitted throughout the entire process. The processing results are then integrated with the output of the multi-dimensional process optimization node model to ultimately generate a business process adaptive optimization foundation model that integrates static configuration and dynamic adaptation, covering cross-module collaboration. In the processing stage, the cross-domain optimization deviation correction model is first used to identify and correct issues such as process gaps, connection breaks, logical conflicts, and parameter mismatches in the multi-dimensional process optimization correlation matrix, ensuring that the optimization logic between different business domains and different process stages remains consistent and unified. Simultaneously, the static process standardization propagation mechanism automatically synchronizes the company's established compliance rules, timeliness thresholds, process baselines, and other static constraints to all nodes throughout the entire process, strengthening the uniformity and enforceability of static rules. After completing the deviation correction and static feature enhancement, the corrected optimization correlation, enhanced static process features, and output results of the multi-dimensional process optimization node model are deeply integrated to form a basic model for adaptive optimization of business processes that combines static standard stability with dynamic scenario adaptability and supports cross-departmental and cross-module collaboration.

[0077] The model inputs include a multi-dimensional process optimization correlation matrix, the weights of each process optimization node, and scenario adaptation calculation results, ensuring the completeness and quantification of the correction basis. The model sequentially performs three core processing operations: gap detection, conflict resolution, and parameter completion. It identifies connection breakpoints between processes, resolves and aligns inconsistencies and logical conflicts in optimization rules, and automatically completes missing process parameters and constraints, ensuring continuous, consistent, and executable process relationships. The output is a corrected process optimization correlation relationship, eliminating process breakpoints and logical conflicts, ensuring a smooth optimization path throughout the entire process.

[0078] This mechanism automatically transmits pre-defined static process standards to all nodes of the entire process, including compliance requirements for graded protection, bidding management standards, data anonymization and storage rules, standard implementation timeliness, and process approval baselines. This ensures that static constraints are reflected and reinforced at every process node, guaranteeing that process optimization does not violate regulations and regulatory bottom lines. The mechanism integrates static process optimization features, dynamic correction results, and multi-dimensional process optimization node model outputs to form the final adaptive optimization model for business processes. This model can simultaneously meet the needs of static standard control and dynamic scenario adjustment, supporting cross-departmental and cross-business domain collaborative operation.

[0079] During process execution, gaps can easily occur between software development and on-site construction, creating optimization gaps. By using a cross-domain optimization deviation correction model, an integration and debugging pre-stage is added between the development and construction phases, ensuring a smooth transition between development outputs and construction inputs, thus bridging these gaps. Simultaneously, a static process standardization propagation mechanism disseminates the requirements of the Cybersecurity Classified Protection 2.0, bidding compliance rules, and data anonymization specifications to all process nodes, including requirements gathering, development, procurement, construction, and acceptance, achieving full coverage of static rules. The resulting adaptive optimization model for the business process possesses dual adaptability to static standards and dynamic scenarios. It can automatically respond to scenario changes, support cross-departmental collaborative scheduling, and receive human experience feedback. It is applicable to all business processes, including customized software development, information system integration, project bidding, hardware and software procurement, on-site construction, operation and maintenance services, and compliance management, providing stable, unified, and reliable model support for subsequent real-time dynamic optimization.

[0080] S106 dynamically optimizes based on the basic model combined with real-time business data streams and process execution deviation correction signals. It balances optimization accuracy and execution efficiency through cross-scenario correlation optimization algorithms, generating a business process optimization solution that combines standardization and personalization to adapt to complex business scenarios.

[0081] In one implementation, real-time acquisition of business data streams and process execution deviation correction signals across all scenarios is used to input these signals into a business process adaptive optimization base model, calculating the real-time optimization adaptability of each stage. The model matches the operational status of each process stage with the optimization objective, obtaining the real-time optimization adaptability of each stage and providing a quantitative basis for determining whether to trigger dynamic adjustments. During the data acquisition phase, continuous real-time data acquisition is conducted across all business scenarios, covering various business processes such as customized software development, information system integration, hardware and software procurement, on-site construction, operation and maintenance services, and compliance management. Deviation correction signals automatically generated during process execution are simultaneously acquired. The collected multi-source data is normalized and time-series aligned before being used as real-time input data for the business process adaptive optimization base model.

[0082] Employing high-frequency data acquisition methods, the system collects comprehensive data in real-time, at the second or minute level, including process execution status, resource usage, cross-departmental collaboration results, compliance verification status, and information system operation indicators. This ensures the data accurately reflects the current process status and provides real-time data for dynamic optimization. The process monitoring unit automatically identifies anomalies and generates deviation correction signals, including process node execution timeouts, resource usage exceeding preset limits, compliance verification failures, cross-departmental collaboration interruptions, and abnormal customer feedback. Each signal corresponds to a specific type of process anomaly and its impact scope. The business process adaptive optimization model compares the real-time collected data with standard process characteristics, optimal operating condition benchmarks, and scenario optimization goals, outputting a fit score between 0 and 1. A score closer to 1 indicates a better match between the current process status and the optimization goal, while a lower score indicates a larger optimization gap.

[0083] In the full-scenario business data flow acquisition, real-time data is acquired on the entire process, including project development progress, system integration and debugging status, equipment procurement and delivery status, on-site construction personnel scheduling, operation and maintenance service response status, and compliance execution status of graded protection. Simultaneously, process execution deviation correction signals are collected, including process anomaly signals such as timeouts in the requirement confirmation stage, delays in equipment procurement cycles, overdue on-site construction periods, and missing graded protection assessment processes. These real-time business data and deviation signals are input into the basic adaptive optimization model of the business process. The model calculates the real-time optimization adaptability of each key node: the adaptability of the requirement confirmation node is low, the adaptability of the procurement execution node is low, the adaptability of the on-site construction node is low, the adaptability of the compliance control node is at a normal level, and the adaptability of the acceptance and delivery node is at a normal level. Based on the above adaptability calculation results, the current optimization shortcomings of the process can be intuitively reflected, providing quantitative input for subsequent trigger threshold verification, execution deviation calibration, and process refactoring, ensuring the accuracy and targeting of dynamic optimization.

[0084] The adaptability of each process is checked against a threshold to determine if dynamic optimization adjustment conditions are triggered. When adjustment is triggered, the deviation characteristics and real-time data of the corresponding process are extracted, and deviation calibration and logic reconstruction are performed through a data-driven algorithm to generate process optimization parameters adapted to the current operating conditions. The real-time optimization adaptability of each process node is checked against a threshold to determine if the triggering conditions for dynamic optimization adjustment are met. When adjustment is triggered, the deviation characteristics and real-time operating data of the corresponding process step are extracted, and deviation calibration and process logic reconstruction are performed through a data-driven algorithm to generate process optimization parameters adapted to the current business operating conditions.

[0085] In the threshold determination stage, the system compares the real-time adaptability calculated for each node with the preset optimization trigger threshold. When a node's adaptability is lower than the threshold, it is determined that the node has a significant optimization gap, meeting the dynamic optimization adjustment trigger condition, and the optimization process is then initiated. After the adjustment is triggered, the system performs in-depth analysis of the abnormal process nodes, extracts the deviation features that lead to low adaptability, and combines real-time collected business data with historical best operating condition data. Through data-driven algorithms, it locates the root cause of the deviation, calculates and quantifies the correction amount, and reconstructs the original process logic to form optimization parameters that meet the current scenario, resource, and efficiency requirements.

[0086] The threshold judgment module takes as input data the real-time optimization adaptability of each process node. The judgment rule compares the real-time adaptability with a preset threshold; if it falls below the threshold, optimization is triggered. The output data is the process optimization trigger signal, including abnormal nodes, deviation levels, and impact ranges. The deviation calibration algorithm takes as input data process deviation characteristics, real-time operating data, and historical best operating condition data. It first identifies the source of the deviation, then calculates the corresponding correction, and finally reconstructs the process execution logic, outputting optimization parameters such as process cycle, resource allocation, collaboration relationships, and compliance control.

[0087] For example, threshold checks were performed on the adaptability of key nodes such as demand confirmation, procurement execution, and on-site construction. The results were all below the optimization trigger threshold, prompting the system to trigger dynamic optimization. Deviation characteristics were extracted from each stage: the demand stage suffered from cumbersome communication and slow confirmation processes; the procurement stage had an overly long supply chain and insufficient inventory; and the construction stage experienced personnel scheduling conflicts and inefficient sequential execution of processes. Based on these deviation characteristics, calibration and logic restructuring were carried out. An online rapid confirmation channel was added to the demand stage to simplify offline interaction processes; an emergency supply channel was activated in the procurement stage, prioritizing inventory allocation for core projects; and key construction processes were adjusted to be executed in parallel, increasing the on-site personnel allocation ratio. Ultimately, optimized parameters adapted to the current working conditions were generated: the demand cycle was significantly compressed, the procurement cycle was significantly shortened, and the construction cycle was effectively reduced. Simultaneously, the compliance assessment process was embedded in the development stage, achieving proactive compliance requirements.

[0088] The process execution configuration is updated, and the calibrated optimization strategy is written into the process scheduling field. The initial efficiency weight is set as a proportion of the current average fit. Based on the optimization parameters obtained from deviation calibration and logic reconstruction, the process execution configuration is updated in real time. The calibrated optimization strategy is uniformly written into the process scheduling field, and the initial efficiency weight is set based on the current average process fit, ensuring that the optimization strategy takes effect immediately throughout the entire process, achieving dynamic adjustment of process scheduling and resource allocation. During the configuration update phase, the optimized process node timeliness, collaborative paths, resource scheduling schemes, compliance verification rules, and other content are synchronously updated to the process execution configuration, ensuring that each step executes according to the optimized logic. The process scheduling field, as the core configuration carrier for process operation, stores key configuration information such as process node execution timeliness, resource quotas, operation permissions, collaborative interaction rules, and compliance constraints, serving as the core basis for automated process execution and scheduling. During the efficiency weight setting phase, automatic allocation is performed based on the real-time average fit of each node. Process steps with higher fit receive higher efficiency weights, enjoying higher execution priority in resource competition and time-series scheduling, thus ensuring that high-fit, high-value processes are executed first.

[0089] The process scheduling field is used to uniformly store configuration information such as execution time, resource configuration, operation permissions, collaboration rules, and compliance requirements of process nodes, providing standardized instructions for automated process execution and supporting real-time writing, dynamic modification, and global application. Efficiency weights are automatically calculated and allocated based on the real-time optimization adaptability of process nodes. The higher the adaptability, the greater the weight, and the higher the level of the corresponding node in resource scheduling, execution sequence, and collaboration priority, ensuring that core links and links with high optimization benefits run first.

[0090] Based on the optimization results, the configurations for each key node are updated: an online rapid confirmation process is implemented in the requirement confirmation stage, with standard processing time limits set; an emergency supply process is implemented in the procurement stage to shorten the delivery cycle; a parallel operation mode is implemented in the construction stage to compress the overall project duration; and in the compliance stage, the information security assessment is executed simultaneously with the development process to achieve compliance in advance. The above optimization rules and configuration parameters are written into the process scheduling fields of the project management platform, procurement management system, construction scheduling system, and compliance control platform, ensuring that each system executes according to a unified optimization strategy. Based on the current average adaptability of the entire process, an initial efficiency weight is set, using efficiency weight as the core allocation basis. Simultaneously, compliance weight and collaboration weight are configured to form a comprehensive weight system that considers efficiency, compliance, and collaboration, enabling the process to balance multi-dimensional optimization goals during execution. Through the above configuration updates and weight settings, the process can be automatically scheduled and executed according to the optimized logic, achieving improved timeliness, balanced resources, smooth collaboration, and controllable compliance, laying the foundation for subsequent optimal process combination selection and closed-loop optimization.

[0091] A cross-scenario optimization algorithm is used to weight and balance the optimization accuracy and execution efficiency of each process, sorting them from high to low based on their overall adaptability to select the optimal process combination. This algorithm is driven by multi-scenario data, using process compliance, logical rationality, and customer demand satisfaction as core indicators of optimization accuracy, and cycle time reduction, improved resource utilization, and reduced collaboration costs as core indicators of execution efficiency. Weighted calculations achieve a balanced optimization of accuracy and efficiency.

[0092] The system integrates data such as process optimization accuracy metrics, execution efficiency metrics, business scenario tags, resource constraints, and process node relationships to provide complete input for weighted calculation. A weighted fusion calculation logic is employed to sum the optimization accuracy and execution efficiency according to preset weights, yielding the overall suitability of the process combination. During the calculation process, scenario tags and resource constraints are simultaneously incorporated to dynamically adjust the suitability results, ensuring the output aligns with the current scenario and resource conditions. The output is a list of process combinations sorted from highest to lowest overall suitability, and the process combination with the best overall capability is selected as the final execution plan.

[0093] Import historical business data from multiple scenarios, including process execution data, optimization records, efficiency metrics, and compliance results, to construct a training dataset. Initialize optimization accuracy weights and execution efficiency weights, and determine the weighting calculation rules. Perform batch calculations on the comprehensive suitability of various process combinations and sort them by numerical value. Validate the algorithm output using actual business execution results; the model is considered converged when the optimal process combination output multiple times consecutively matches the actual optimal effect.

[0094] Optimization accuracy primarily reflects process compliance, node logic completeness, customer requirement fulfillment, and process execution stability. Execution efficiency primarily reflects the effect of shortening process cycle time, reducing resource consumption, and lowering cross-departmental collaboration costs. The algorithm integrates these two types of indicators with equal weight to calculate a comprehensive fit that balances quality and efficiency. In typical business scenarios, after calculating and ranking the comprehensive fit of multiple process combinations, the highest-ranked combination is selected as the optimal solution. This solution typically includes optimization methods such as online process acceleration, emergency supply chain scheduling, parallel process execution, and pre-compliance assessment, which can improve efficiency while meeting compliance and customer requirements. Through the above algorithm and selection mechanism, the optimal process combination balancing accuracy and efficiency can be automatically output in complex multi-scenario environments, providing stable and reliable output results for subsequent closed-loop iteration and process solution generation.

[0095] By organically combining real-time data injection, deviation correction, and accuracy-efficiency balancing logic, a complete adaptive optimization closed loop for the process is formed. This loop continuously iterates and outputs updated results containing standardized process templates and personalized scenario adaptation rules, characterizing the dynamic adaptive capability of the business process under complex business scenarios. Based on real-time data-driven operation across the entire process, this closed loop achieves self-running, self-correcting, and self-iterable process optimization through continuous collection, calculation, judgment, calibration, updating, and feedback, enabling the process to maintain stable and efficient operation under changing business conditions.

[0096] The system operates continuously within a closed-loop chain: "real-time data acquisition - adaptation calculation - optimization trigger judgment - deviation calibration - process configuration update - new round of data acquisition," enabling dynamic cycle and continuous improvement of process optimization. Any fluctuations or anomalies in any stage can be quickly detected, automatically corrected, and implemented in real time through the closed-loop system, ensuring the process remains in optimal condition. A unified and universally applicable process baseline is established across all business domains, including standard execution paths, compliance baseline requirements, efficiency benchmark indicators, and resource configuration specifications. This provides a unified, stable, and reusable execution framework for all business processes, ensuring compliance, stability, and controllability. Differentiated adaptation rules are set for different business scenarios, automatically switching process strategies based on project scale, peak business periods, compliance intensity, and construction urgency. This ensures standardization while meeting the personalized needs of complex scenarios, achieving dual optimization of "standard as the foundation, scenario adaptation."

[0097] During closed-loop operation, real-time business data is continuously fed into model calculations, process adaptability is updated in real time, deviation signals are identified and calibrations are triggered in real time, and process configurations are dynamically updated based on optimization results. The system outputs two core deliverables based on the closed loop: First, standardized process templates, including unified execution steps, timing nodes, compliance verification points, resource configuration standards, and delivery and acceptance standards, applicable to all routine business scenarios, ensuring standardized and consistent process execution. Second, personalized scenario adaptation rules, automatically activating exclusive rules for special scenarios such as large-scale projects, peak business periods, high compliance requirements, and emergency construction, including enhanced compliance control, parallel processes, emergency resource scheduling, pre-qualification verification, and full-process auditing, achieving precise scenario-based adaptation.

[0098] Based on standardized templates and personalized rules derived from closed-loop iterative output, a business process optimization solution is further integrated and can be directly implemented. This solution, grounded in standardization and extended with personalization, balances universality and flexibility, and can stably adapt to complex and ever-changing business scenarios. In actual operation, it achieves multi-dimensional optimization effects: significantly reduced process execution cycles, comprehensive improvement in compliance control coverage and compliance rates, increased cross-departmental collaboration efficiency and reduced connection costs, more balanced and reasonable resource allocation, and overall process maintaining efficient, stable, and compliant operation amidst dynamic changes. Through the aforementioned closed-loop mechanism and solution output, processes can automatically sense scenario changes, automatically identify execution deviations, automatically complete optimization adjustments, and automatically update configuration strategies without manual intervention. This fully demonstrates the dynamic adaptive capability of business processes in complex scenarios, providing support for the intelligent and efficient operation of all enterprise business processes.

[0099] In one implementation, such as Figure 2 As shown, this application also provides a data-driven adaptive optimization system for enterprise-side business processes, including: The process data acquisition module 201 is used to collect enterprise full-link business data, covering process node execution data, cross-departmental collaborative interaction data, business scenario characteristic data, resource scheduling and configuration data, customer feedback interaction data, and system operation status data. The process feature parsing module 202 is used to parse business data based on a data-driven process feature extraction algorithm, identify process bottleneck attributes by combining efficiency thresholds and compliance indicators, and generate a basic dataset of business processes adapted to the scenario by triggering the optimization mode switch through scenario-process adaptation rules. The associated feature aggregation module 203 is used to classify the basic dataset according to core business and supporting business, construct multi-dimensional process association nodes for each category, aggregate process optimization features through cross-module data association algorithm, and generate a bidirectional mapping optimization association graph oriented to business processes. The multi-dimensional verification modeling module 204 is used to model based on a multi-dimensional process verification model, mine the logical consistency relationship of process optimization under various business scenarios, and generate process optimization verification feature vectors. The adaptive engine building module 205 is used to import feature vectors into the business process adaptive optimization engine. It adopts a static process standard configuration mechanism, introduces a dynamic scenario adaptation calibration module, and combines a human experience feedback synchronization mechanism to generate a process optimization update link, forming a basic model for business process adaptive optimization that integrates static and dynamic adaptation and cross-module collaboration. The dynamic optimization output module 206 is used to perform dynamic optimization based on the basic model combined with real-time business data flow and process execution deviation correction signals. It balances optimization accuracy and execution efficiency through cross-scenario correlation optimization algorithms to generate a business process optimization solution that combines standardization and personalization to adapt to complex business scenarios.

[0100] An electronic device includes a first processor and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute any data-driven enterprise business process adaptive optimization method by executing the executable instructions.

[0101] A computing device includes a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute any data-driven adaptive optimization method for enterprise business processes.

[0102] The methods and / or embodiments in this application can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. When the computer program is executed by a processing unit, it performs the functions defined in the methods of this application.

Claims

1. A data-driven adaptive optimization method for enterprise-side business processes, characterized in that, include: Collect enterprise full-chain business data, covering process node execution data, cross-departmental collaborative interaction data, business scenario characteristic data, resource scheduling and configuration data, customer feedback interaction data, and system operation status data; The data-driven process feature extraction algorithm analyzes business data, identifies process bottleneck attributes by combining efficiency thresholds and compliance indicators, and triggers optimization mode switching through scenario-process adaptation rules to generate a basic dataset of business processes adapted to the scenario. The basic dataset is classified into core business and supporting business, and multi-dimensional process association nodes are constructed for each category. Process optimization features are aggregated through cross-module data association algorithms to generate a bidirectional mapping optimization association graph oriented towards business processes. Based on multi-dimensional process verification modeling, we can explore the logical consistency relationship of process optimization in various business scenarios and generate process optimization verification feature vectors. The feature vectors are imported into the business process adaptive optimization engine. A static process standard configuration mechanism is adopted, a dynamic scenario adaptation calibration module is introduced, and a process optimization update link is generated by combining a human experience feedback synchronization mechanism, forming a basic model for business process adaptive optimization that integrates static and dynamic adaptation and cross-module collaboration. Dynamic optimization is performed based on a basic model combined with real-time business data streams and process execution deviation correction signals. By using cross-scenario correlation optimization algorithms to balance optimization accuracy and execution efficiency, a business process optimization solution that combines standardization and personalization to adapt to complex business scenarios is generated.

2. The data-driven adaptive optimization method for enterprise-side business processes according to claim 1, characterized in that, A data-driven process feature extraction algorithm analyzes business data, identifies process bottleneck attributes by combining efficiency thresholds and compliance indicators, and triggers optimization mode switching through scenario-process adaptation rules to generate a basic dataset of scenario-adapted business processes, including: By combining the enterprise's full-chain business data collection results with process node status monitoring data, efficiency threshold judgment rules and compliance indicator analysis mechanisms are introduced to dynamically identify bottleneck characteristics of business process data and generate process bottleneck attribute classification results. By combining the optimization and adaptation requirements of business scenarios with the enterprise process control standards, the classification results of process bottleneck attributes are processed to complete the optimization mode matching of core business processes and supporting business processes, and generate optimization mode switching trigger signals. Based on business efficiency threshold standards, process node status change information, and optimization mode switching generation rules, the optimization mode switching trigger signal is systematically verified to achieve accurate switching of business process optimization modes in multiple scenarios. Based on the full-chain business characteristics and departmental collaborative interaction relationships, the corresponding optimization dimensions are automatically matched, and an abnormal matching is marked and the pattern is corrected based on the optimization mode and process attribute adaptability detection mechanism. According to the preset business process data integration rules, the categorized process information is associated and integrated with the matching optimization mode and the corresponding dimension of business source data to generate a scenario-adapted basic dataset of business processes.

3. The data-driven adaptive optimization method for enterprise-side business processes according to claim 2, characterized in that, The basic dataset is categorized into core business and supporting business. For each category, multi-dimensional process-related nodes are constructed. Process optimization features are aggregated using a cross-module data association algorithm to generate a bidirectional mapping optimization association graph oriented towards business processes, including: The basic dataset of business processes is processed and classified into core business and supporting business. Each type of data includes process nodes, collaborative relationships, resource configuration, scenario characteristics and efficiency indicators. Dynamic features such as process time, interaction frequency, resource consumption and compliance deviation are aggregated through dynamic feature windows. Data on process execution, collaborative interaction and scheduling control of each type of process are extracted to generate corresponding subsets. With core business as the core node, supporting business as the collaborative node, and process optimization as the target node, node attribute information is generated by combining process mapping and scenario recognition, and edge association information is generated according to data correlation and optimized response time sequence. Construct a bidirectional mapping directed graph that includes core business nodes across the entire chain, collaborative nodes supporting multiple types of business, and differentiated optimization judgment nodes. Strengthen the feature coupling strength between processes through a cross-module association enhancement mechanism. Based on a data-driven multi-module coupling model combined with feedforward prediction, feedback calibration, and dynamic adaptation verification modes, input corresponding subset data to calculate optimization parameter configuration and feature aggregation algorithm parameters. Based on business attributes and process features, transform the solution and generate an integrated business process bidirectional mapping optimization association graph. By comparing and analyzing the basic process data with historical best operating condition data, and by intelligently linking various business modules to perform online analysis, feedback calibration is performed based on process efficiency deviations, measured values ​​of collaboration costs, and scenario adaptation feedback data, combined with multi-dimensional data mapped in real time by the data-driven model, and the correlation diagram is iteratively optimized.

4. The data-driven adaptive optimization method for enterprise-side business processes according to claim 1, characterized in that, The feature vectors are imported into the business process adaptive optimization engine. A static process standard configuration mechanism is adopted, a dynamic scenario adaptation and calibration module is introduced, and a process optimization update chain is generated by combining a human experience feedback synchronization mechanism. This forms a basic model for business process adaptive optimization that integrates static and dynamic adaptation and cross-module collaboration, including: The process optimization verification feature vector is combined and matched with the business process attribute features. A static process standard configuration algorithm and a cross-module optimization feature aggregation mechanism are introduced to realize cross-domain mapping and feature fusion of business process optimization. Align with enterprise business management standards and static and dynamic scenario optimization requirements, construct a multi-dimensional process optimization node model, calculate optimization mapping weights through cross-domain adaptation precision calculation model, and establish a dynamic optimization association mechanism for business processes; With optimizing the tightness of association as the core dimension, a multi-dimensional process optimization association matrix is ​​constructed by integrating static process configuration data, dynamic scenario calibration data, cross-module collaborative interaction data, and human experience feedback data. By addressing optimization gaps through a cross-domain optimization deviation correction model, strengthening the static process optimization correlation characteristics by combining a static process standardization propagation mechanism, and integrating the results of a multi-dimensional process optimization node model, a basic model for adaptive optimization of business processes with integrated static and dynamic adaptation and cross-module collaboration is generated.

5. The data-driven adaptive optimization method for enterprise-side business processes according to claim 4, characterized in that, Dynamic optimization is performed based on a fundamental model combined with real-time business data streams and process execution deviation correction signals. A cross-scenario correlation optimization algorithm balances optimization accuracy and execution efficiency, generating standardized yet personalized business process optimization solutions adapted to complex business scenarios, including: Real-time acquisition of business data streams and process execution deviation correction signals across the entire scenario, inputting them into the basic model for adaptive optimization of the business process, and calculating the real-time optimization adaptability of each link; Threshold verification is performed on the adaptability of each process to determine whether dynamic optimization adjustment conditions are triggered. When adjustment is triggered, the deviation characteristics and real-time data of the corresponding process are extracted, and deviation calibration and logic reconstruction are performed through data-driven algorithms to generate process optimization parameters that are adapted to the current working conditions. Update the process execution configuration, write the calibrated optimization strategy into the process scheduling field, and set the initial efficiency weight to the corresponding proportion of the current average fit. The optimization algorithm for cross-scenario association is used to weight and balance the optimization accuracy and execution efficiency of each process, and the optimal process combination is selected by sorting them from high to low according to their comprehensive adaptability. By combining real-time data injection, deviation correction, and accuracy-efficiency balance logic to form a closed loop, an update result containing standardized process templates and personalized scenario adaptation rules is generated to characterize the dynamic adaptive capability of the process to adapt to complex business scenarios. Based on the update results, which include standardized process templates and personalized scenario adaptation rules, a business process optimization solution that combines standardization and personalization is generated to adapt to complex business scenarios.

6. A data-driven adaptive optimization system for enterprise-side business processes, characterized in that, The system is used to execute executable instructions to perform the method of any one of claims 1 to 5.

7. An electronic device, characterized in that, include: First processor; And a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of any one of claims 1 to 5 by executing the executable instructions.

8. A computing device comprising a memory for storing computer program instructions and a second processor for executing the computer program instructions, wherein, When the computer program instructions are executed by the second processor, the device is triggered to perform the method of any one of claims 1 to 5.

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