Data-driven hydraulic engineering cost dynamic closed-loop control method

Abstract

A data-driven hydraulic engineering cost dynamic closed-loop control method comprises the following steps: S1, acquiring and analyzing project budget data, project price settlement data and accounting subject system data, and integrating the data into a central data resource library; s2, converting the price settlement data into structured accounting voucher data; s3, calculating a cost deviation analysis result reflecting the difference between the actual cost and the budget plan according to the project budget data and the accounting subject data; s4, based on the cost deviation analysis result, a control instruction used for indicating subsequent operation is generated; s5, in response to the control instruction, automatically executing a preset control operation; according to the method, the business and finance integrated data footstone is constructed, the prospective controllability model is combined, hierarchical adaptive closed-loop control is driven, and the accuracy and automation level of decision advance and risk management and control are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of financial management technology for engineering projects, specifically a data-driven dynamic closed-loop control method for the cost of water conservancy projects. Background Technology

[0002] In the field of large-scale infrastructure project management, digital transformation and data-driven decision-making have become core trends for improving management efficiency. Particularly for water conservancy projects with large investment scales, long construction periods, and numerous uncertainties, how to utilize next-generation information technology to achieve refined, dynamic, and intelligent closed-loop control of core project elements (especially costs) has become a key technological direction of concern for the industry. This requires technical solutions that can not only handle massive amounts of heterogeneous data but also extract forward-looking insights to support proactive and precise management prevention.

[0003] In current technological practices, cost control in water conservancy projects primarily relies on multiple independent software systems or management modules, such as cost estimation management systems, project settlement systems, and financial accounting systems. While these systems function within their respective professional fields, achieving comprehensive and dynamic closed-loop cost control still faces the following areas requiring further improvement:

[0004] Challenges to Data Consistency and Process Collaboration: The data sources for the three key stages of preliminary budgeting, settlement, and financial accounting are relatively independent, and the interaction between systems usually relies on manual data export, verification, and import. This model limits the efficiency and accuracy of data flow to some extent, posing challenges to ensuring the consistency of the final "three sets of accounts" data, and also leaving room for improvement in cross-departmental business process collaboration.

[0005] The Lag in Cost Risk Assessment: Existing cost monitoring methods typically focus on comparing actual costs with budgets, i.e., judging static deviations by thresholds. The limitation of this approach is that it assesses the "results" that have already occurred, lacking effective quantitative analysis tools for the evolutionary trend of cost deviations (e.g., whether the deviation is accelerating or converging). Therefore, risk signals often exhibit a certain lag, making it difficult for managers to obtain timely warnings at the nascent stage of cost risks.

[0006] Therefore, there is an urgent need for a data-driven dynamic closed-loop control method for water conservancy project costs to improve the level of precision in water conservancy project cost control. Summary of the Invention

[0007] The technical problem this invention aims to solve is to provide a data-driven dynamic closed-loop control method for water conservancy project costs. This method is achieved through a controllability assessment model that integrates static deviation analysis and dynamic trend prediction. The controllability assessment model, acting as a central decision engine, integrates previously fragmented and delayed cost management activities into a real-time, interconnected, and intelligent cost management activity. This significantly improves the early warning capability for cost overrun risks, effectively shortens the response delay from risk identification to management intervention, and enhances the accuracy and foresight of project cost status assessment. Ultimately, it achieves a higher level of refinement in water conservancy project cost control.

[0008] The technical solution of this invention is as follows:

[0009] A data-driven dynamic closed-loop control method for water conservancy project costs includes the following steps:

[0010] S1: Acquire and parse project budget data, engineering payment settlement data, and accounting subject system data, and integrate the project budget data, engineering payment settlement data, and accounting subject system data into the central data resource library according to the preset unified data model;

[0011] S2: Map the project payment settlement data to the rule base of accounting subjects according to the project settlement project type, and convert the payment settlement data into structured accounting voucher data;

[0012] S3: Real-time correlation between project budget data and accounting data in the central data resource library to calculate cost deviation analysis results that reflect the difference between actual costs and budget plans;

[0013] S4: Based on the cost deviation analysis results, generate control instructions to indicate subsequent operations;

[0014] S5: Responds to control commands and automatically executes preset control operations.

[0015] As a preferred technical solution, the steps in S1 for integrating project budget data, engineering payment settlement data, and accounting subject system data in the unified data model are as follows:

[0016] An integrated server containing three components—data extraction, data processing, and data loading—extracts project budget data, engineering cost settlement data, and accounting subject system data from different sources and converts them into a unified XML format. Then, it loads the data into the central data resource library according to a unified data model.

[0017] As a preferred technical solution, the data transformation in S2 maps each project settlement item type to a financial dimension that includes accounting subjects, cost centers, and budget items through a rule base.

[0018] As a preferred technical solution, the steps in S3 regarding the cost deviation analysis results further include: based on the cost deviation analysis results, calculating and outputting a comprehensive controllability index to characterize the current cost control status of the project through a preliminary cost controllability assessment model.

[0019] As a preferred technical solution, the steps for generating the comprehensive controllability index are as follows:

[0020] The cost deviation rate, investment rate deviation, and cost deviation momentum in the cost deviation analysis results are normalized to obtain three standardized risk factors: cost deviation risk factor, investment rate risk factor, and momentum risk factor.

[0021] The cost deviation risk factor, investment rate risk factor, and momentum risk factor are integrated into a single comprehensive risk score.

[0022] Based on the comprehensive risk score, the final comprehensive controllability index is calculated.

[0023] As a preferred technical solution, the steps for generating control commands are as follows:

[0024] Input the comprehensive controllability index, which is used to calculate the relevant parameters of the dynamic threshold, including the baseline first-level threshold, the baseline second-level threshold, and the project stage adjustment coefficient;

[0025] The current dynamic primary threshold and dynamic secondary threshold are calculated based on the baseline primary threshold, the baseline secondary threshold, and the project stage adjustment coefficient.

[0026] As a preferred technical solution, the logical judgment and signal generation of control commands are as follows:

[0027] Judgment condition 1: Determine whether the comprehensive controllability index is greater than the dynamic first-level threshold;

[0028] Execution path one: If condition one is true, generate a risk status signal and set its value to normal;

[0029] Judgment Condition 2: If Condition 1 is false, then continue to judge whether the comprehensive controllability index is greater than the dynamic secondary threshold;

[0030] Execution path two: If condition two is true, then generate a risk status signal and set its value to "attention";

[0031] Execution path three: If condition two is also false, then generate a risk status signal and set its value to warning;

[0032] The final output is a risk status signal with a value of normal, attention, or warning.

[0033] As a preferred technical solution, the preset control operation performed in S5 is as follows: when a risk status signal at the warning level is received, a preset payment freeze workflow is automatically triggered to temporarily suspend the engineering payment function of the cost center associated with the risk status signal at the warning level; and if the payment freeze workflow fails to execute, a backup notification mechanism is automatically triggered to send alarm information to the preset supervisors.

[0034] As a preferred technical solution, the specific steps for performing the control operation are as follows:

[0035] Input the risk status signal, and the context information of the project or section associated with the risk status signal;

[0036] Hierarchical adaptive control:

[0037] Judgment condition 1: Determine whether the value of the risk status signal is normal;

[0038] Execution Path 1: No active control operation is performed; the controllability status is only displayed in green on the user interface.

[0039] Judgment Condition 2: Determine whether the value of the risk status signal is of concern;

[0040] Execution Path Two: Triggering a workflow that escalates alerts and enhances approval processes. This workflow specifically performs two actions:

[0041] Action 2a: Send cost concern notifications to project managers and cost managers associated with the cost center through the built-in message center or the integration interface with office software such as DingTalk and WeChat.

[0042] Action 2b: Automatically modify the approval template for subsequent project payment processes related to the cost center; dynamically add a project chief engineer or financial manager as an additional approval node based on the original approval nodes;

[0043] Judgment Condition 3: Determine whether the value of the risk status signal is a warning;

[0044] Execution Path 3: Workflow that triggers rigid payment circuit breakers and emergency responses.

[0045] As a preferred technical solution, the workflow specifically performs the following actions:

[0046] Action 3a: By calling the API of the permission management module, the permissions to initiate and approve payments for all user roles associated with the cost center are temporarily disabled; this operation directly stops the outflow of funds from the risk area at the system level.

[0047] Action 3b: Immediately query the permission management database or call the query interface to confirm whether the permission change in Action 3a has been successfully written and taken effect;

[0048] Action 3c: If and only if the verification result of Action 3b fails, the backup notification mechanism shall be triggered immediately. The notification mechanism shall send a specially formatted high-priority alarm message to the mobile phone number and email address of the highest-level supervisor in the system configuration by calling the independent SMS gateway HTTP API or SMTP email service interface. The message content must include: project name, cost center, current risk level, control measures and critical status.

[0049] Advantages of this invention:

[0050] This invention fundamentally solves the long-standing problems of data silos and business process fragmentation in water conservancy project cost management by constructing a unified data model and an intelligent conversion engine, achieving integrated and automated processing of business and financial data. Utilizing component-based adapters and a standardized XML intermediate format, it establishes seamless connections between heterogeneous data sources, ensuring consistency and timeliness of budget, settlement, and financial data. Through an intelligent conversion engine with an embedded user-configurable rule base, the complex process of converting project settlements to accounting vouchers, which previously relied on the professional experience of financial personnel, is transformed into a deterministic operation that can be automatically executed by machines. This not only significantly improves the efficiency of data processing and the accuracy of cost aggregation but also provides a high-quality and reliable data foundation for all subsequent advanced analysis and intelligent control. Furthermore, it effectively improves the quality and efficiency of final financial statement preparation, increasing the efficiency of final financial statement preparation by 30% compared to traditional methods.

[0051] This invention proposes a cost execution controllability assessment model with forward-looking predictive capabilities, realizing a revolutionary shift from static, lagging post-event monitoring to dynamic, forward-looking pre-event early warning. It introduces the key indicator of cost deviation momentum, enabling the model to not only assess the current state but also predict future trends. By using the logistic function to nonlinearly normalize multi-dimensional risk factors such as static deviation and dynamic momentum, and combining this with dynamically adjusted risk thresholds, a highly accurate comprehensive controllability index that adapts to the risk characteristics of different project stages can be generated. This allows managers to receive accurate early warnings when cost risks are still in their nascent stages, significantly improving the sensitivity and lead time for risk identification and gaining valuable decision-making time for intervention measures.

[0052] This invention, based on forward-looking risk assessment, constructs a hierarchical, adaptive, automated closed-loop control management method, elevating the cost management of water conservancy projects from a traditional, passive response paradigm to a new level of proactive, intelligent intervention. It hard-links three risk status signals—normal, watchful, and early warning—with clear and specific control operations, achieving a seamless closed loop from risk perception to management action. For watchful risks, a flexible intervention approach with enhanced approval workflows is adopted, accurately correcting deviations without disrupting operations. For early warning risks, a rigid payment circuit breaker is decisively implemented, along with a degradation protection mechanism for emergency response, ensuring robust control. The design, combining in-depth analysis with automated control, transforms project cost management from an open-loop model relying on manual judgment and offline coordination into an intelligent closed-loop ecosystem capable of self-perception, self-diagnosis, and self-adjustment, achieving a synergistic leap in management efficiency and risk control capabilities. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the steps of the present invention;

[0054] Figure 2 This is a schematic diagram of the steps for generating the comprehensive controllability index of the present invention;

[0055] Figure 3 This is a schematic diagram of the logic judgment and signal generation process of the control commands of the present invention;

[0056] Figure 4 This is a flowchart illustrating the steps of the execution control operation of the present invention;

[0057] Figure 5 This is a schematic diagram of the specific execution flow of the workflow of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] refer to Figures 1 to 5 :

[0060] A data-driven dynamic closed-loop control method for water conservancy project costs includes the following steps:

[0061] S1: Acquire and parse project budget data, engineering payment settlement data, and accounting subject system data, and integrate the project budget data, engineering payment settlement data, and accounting subject system data into the central data resource library according to the preset unified data model;

[0062] The steps for integrating project budget data, engineering cost settlement data, and accounting subject system data in the unified data model of S1 are as follows:

[0063] An integrated server containing three components—data extraction, data processing, and data loading—extracts project budget data, engineering cost settlement data, and accounting subject system data from different sources and converts them into a unified XML format. Then, it loads the data into the central data resource library according to a unified data model.

[0064] Detailed implementation: A service-oriented architecture is adopted, and the integration server has pre-built component adapters for different data sources (in this embodiment, including the budget software database, Excel settlement sheets, and financial system interfaces). When a new data source needs to be integrated, only the corresponding extraction component adapter needs to be configured, without writing any code. All extracted data, whether structured database tables or semi-structured documents, will be uniformly converted into a predefined XML format by the processing component. The XML format schema strictly follows the definition of the unified data model, ensuring that all data entering the central data resource repository has a consistent structure and semantics.

[0065] By using component-based adapters and a standardized XML intermediate format, "plug-and-play" integration of heterogeneous data sources is achieved, greatly improving scalability and maintainability. It fundamentally solves the data silo problem, ensures the standardization and consistency of data entering the system, and provides a high-quality data foundation for reliable automated processing.

[0066] S2: Map the project payment settlement data to the rule base of accounting subjects according to the project settlement project type, and convert the payment settlement data into structured accounting voucher data;

[0067] In S2, data transformation maps each project settlement item type to a financial dimension that includes accounting subjects, cost centers, and budget items through a rule base.

[0068] Detailed Implementation: Data transformation is performed through an intelligent conversion engine. The rule base embedded in the intelligent conversion engine consists of user-configured relational tables. Taking the execution of "Government Accounting" as an example, in this embodiment, the user can set a rule: {Engineering Settlement Item: C30 Concrete Pouring} maps to {Accounting Subject: 1613-Construction in Progress-Main Structure, Cost Center: XX Section, Funding Source: Central Budgetary Investment}. When a settlement data in XML format is received, it contains... <item name="C30混凝土浇筑">When tags are applied, the conversion engine automatically matches this rule and generates a structured accounting voucher that clearly records the accounting subject, amount, and corresponding cost center and capital attributes.

[0069] This constraint concretizes the vague intelligent transformation process, transforming the complex mental labor that originally relied on the professional experience of financial personnel into deterministic operations that can be automatically executed by machines by defining the mapping logic of the rule base.

[0070] S3: Real-time correlation between project budget data and accounting data in the central data resource library to calculate cost deviation analysis results that reflect the difference between actual costs and budget plans;

[0071] The steps in S3 for analyzing cost deviations further include: based on the cost deviation analysis results, calculating and outputting a comprehensive controllability index to characterize the current cost control status of the project by using a preliminary cost controllability assessment model;

[0072] The key parameters are defined, obtained, and determined as follows:

[0073] The project budget data is an approved, complete cost estimate document that serves as the maximum limit for project investment control, and its data structure is standardized according to a unified data model;

[0074] Specific acquisition method: Extract from the database of the budget preparation software or its exported standard files through the component adapter and load it into the central data resource library;

[0075] The project payment settlement data is jointly confirmed by the supervisor and the construction unit, and reflects the payment application document for the completed work within a specific settlement period. Its data structure has also been standardized into a unified data model format.

[0076] Specific acquisition method: Obtain from the project management system or uploaded spreadsheet (Excel format is used in this embodiment) through file parsing adapter or interface adapter, and load it into the central data resource library;

[0077] The accounting voucher data is automatically generated from project payment settlement data and conforms to accounting standards.

[0078] Specific acquisition method: Financial fact tables stored in the central data resource database;

[0079] The parameter symbol for cost deviation rate is: : Represents the degree of deviation between the cumulative actual engineering costs incurred up to the current reporting period and the cumulative budget plan for the same period; a positive value indicates cost overrun, and a negative value indicates cost surplus;

[0080] Specific acquisition methods and determination procedures: Accumulated actual costs are obtained by summarizing data from accounting vouchers according to cost centers and time dimensions. From the project budget data, based on the same cost center and time dimension, the cumulative estimated planned cost for the same period can be queried or calculated. Cost deviation rate The calculation logic is as follows: Accumulate the actual cost Divide by the cumulative estimated planned cost for the same period Then subtract one from the resulting quotient;

[0081] The sign of the parameter for the deviation in investment rate is: This parameter represents the degree of deviation between the actual investment amount completed during the current reporting period and the planned investment amount for the current period; it is used to measure the matching between the rate of capital consumption and the progress of the project.

[0082] Specific acquisition methods and determination procedures: The actual investment amount completed in the current period is obtained by summarizing the project payment settlement data. The planned investment amount for the current period can be obtained from the project's budget data or annual investment plan. Investment rate deviation The calculation logic is as follows: the actual investment amount completed in the current period Divide by the planned investment amount for the current period Then subtract one from the resulting quotient;

[0083] The parameter sign of cost deviation momentum is Characterizing cost deviation rate The trend and rate of change are forward-looking risk indicators; a positive value indicates that cost overruns are accelerating, while a negative value indicates that the cost situation is improving.

[0084] Specific acquisition methods and determination criteria: Obtain the cost deviation rate for the current reporting period. Secondly, the cost deviation rate for the previous reporting period is retrieved from the central data resource database. Cost Deviation Momentum The calculation logic is as follows: the cost deviation rate for the current reporting period. Subtract the cost deviation rate of the previous reporting period ;

[0085] The parameter symbol for the comprehensive controllability index is: This is a comprehensive evaluation index that integrates static deviation and dynamic trend. Its value range is strictly limited to the range of zero to one. The closer the value is to one, the more controllable the project cost control is and the lower the future risk. The closer it is to zero, the higher the risk.

[0086] The specific steps are as follows:

[0087] Input three basic parameters: cost deviation rate Investment rate deviation and cost deviation momentum ;

[0088] Since the three input parameters have different dimensions and ranges, they must first be normalized for effective fusion, including the cost deviation rate. Investment rate deviation and cost deviation momentum They are uniformly mapped to the range of zero to one, forming standardized risk factors;

[0089] Nonlinear normalization is performed using the logistic function. The mathematical idea of ​​the logistic function originates from the logistic regression model. It can smoothly map any real number input to an open interval between zero and one, and is sensitive to input changes near the zero point, and has a good suppression effect on extreme values.

[0090] The calculation logic of the normalized risk factor is as follows: take the natural constant e to the power of negative k times the input parameter, add one to this power value, and finally take its reciprocal. The parameter k is an adjustable steepness coefficient used to control the steepness of the normalization curve. The value of the parameter k is calibrated through offline experiments and is between 5 and 10.

[0091] Specific implementation: Cost deviation rate... Investment rate deviation and cost deviation momentum Performing the above normalization logic yields three standardized risk factors: cost deviation risk factor. Investment rate risk factor and momentum risk factor ;

[0092] Cost deviation risk factor Investment rate risk factor and momentum risk factor Integrate into a single comprehensive risk score ;

[0093] Comprehensive Risk Score The calculation logic is as follows: Cost deviation risk factor Multiply by its corresponding cost deviation weight Investment rate risk factor Multiply by the corresponding investment rate weight Momentum risk factor Multiply by its corresponding momentum weight Then add these three products together; all weight coefficients must be positive, and their sum must be one.

[0094] Based on comprehensive risk score Calculate the final comprehensive controllability index. ;

[0095] Comprehensive controllability index The calculation logic is as follows: subtract the comprehensive risk score from one. ;

[0096] The final output is a scalar value – the comprehensive controllability index. Comprehensive controllability index The range is between (0,1), which represents the cost status of the project and is used for subsequent early warning, decision support and linkage control.

[0097] S4: Based on the cost deviation analysis results, generate control instructions to indicate subsequent operations;

[0098] The parameters are set as follows:

[0099] The parameter symbol for the baseline level 1 threshold is: The baseline controllability index threshold used to distinguish between normal and high-risk levels;

[0100] Specific acquisition methods and determination procedures: Collect controllability index data for all completed projects whose final costs did not exceed the budget, forming a controllable sample set; calculate the percentile of the controllable sample set. In this embodiment, the fifteenth percentile of the controllable sample set is used as the baseline first-level threshold. The underlying logic is that, historically, the index of 85% of controllable projects has been higher than this value;

[0101] Example of numerical determination: If statistical analysis of a historical controllable sample set yields a 15th percentile of 0.82, then the baseline first-level threshold is... The value was determined to be 0.82;

[0102] The parameter symbol for the baseline secondary threshold is: The baseline controllability index threshold used to distinguish between risk levels of concern and those of early warning;

[0103] Specific acquisition methods and determination procedures: Collect controllability index data for all completed projects with significant cost overruns (overrun rate greater than 10% in this embodiment) across all periods to form a risk sample set. Calculate the percentile of the risk sample set. In this embodiment, the 80th percentile of the risk sample set is taken as the baseline secondary threshold. The underlying logic is that historically, the indices of 80% of out-of-control projects were below this value.

[0104] Example of numerical determination: If statistical analysis of a historical risk sample set yields an 80th percentile of 0.65, then the baseline secondary threshold is... The value was determined to be 0.65;

[0105] The parameter symbol for the project phase adjustment factor is: : A coefficient used to dynamically adjust the risk assessment threshold according to different construction stages of the project;

[0106] Specific acquisition methods and determination methods: Based on the general principles of project management, offline calibration is carried out through piecewise functions or lookup tables; the core idea is that in the early and late stages of a project, uncertainty is high and the risk tolerance should be low; in the middle stage of a project, large-scale construction is proceeding stably, and the tolerance can be appropriately relaxed.

[0107] Obtain the cumulative investment completion rate of the project Based on the range in which the completion rate falls, determine the project stage adjustment coefficient. The value;

[0108] Example of determining a value: The following lookup table can be set up:

[0109] If the cumulative investment completion rate is in the range of [0, 20%) (early stage of the project), then the project stage adjustment coefficient is... The value is 1.05;

[0110] If the cumulative investment completion rate is in the range of [20%, 80%) (mid-term of the project), then the project stage adjustment coefficient... The value is 1.00;

[0111] If the cumulative investment completion rate is in the range of [80%, 100%] (project end / final stage), then the project stage adjustment coefficient... The value is 1.10;

[0112] The parameter symbol for the dynamic first-level threshold is: After dynamic adjustment, the first-level threshold actually used for judging the current risk level;

[0113] Specific acquisition methods and determination procedures: The baseline first-level threshold... Multiply by the project phase adjustment factor ;

[0114] The parameter symbol for the dynamic secondary threshold is: The secondary threshold used for determining the current risk level after dynamic adjustment;

[0115] Specific acquisition methods and determination procedures: The baseline secondary threshold... Multiply by the project phase adjustment factor ;

[0116] The parameter symbol for the risk status signal is: : A discrete control instruction used to characterize the current project cost risk level, with a value of one of normal, attention or warning;

[0117] Specific acquisition method: through the comprehensive controllability index This is derived by comparing it with a dynamic threshold.

[0118] The steps for generating control commands are as follows:

[0119] Input comprehensive controllability index Parameters used to calculate dynamic thresholds, including the baseline first-level threshold. , benchmark secondary threshold Project phase adjustment coefficient ;

[0120] Calculate the dynamic threshold: based on the baseline first-level threshold , benchmark secondary threshold Project phase adjustment coefficient Calculate the current dynamic first-level threshold and dynamic secondary threshold ;

[0121] Logical judgment and signal generation:

[0122] Judgment Condition 1: Judge the overall controllability index Is it greater than the dynamic first-level threshold? ;

[0123] Execution Path 1: If condition 1 is true, then generate a risk status signal. And set its value to normal;

[0124] Judgment Condition 2: If Condition 1 is false, then continue to judge the comprehensive controllability index. Is it greater than the dynamic secondary threshold? ;

[0125] Execution path two: If condition two is true, then generate a risk status signal. And set its value to "Follow";

[0126] Execution path three: If condition two is also false, then generate a risk status signal. And set its value as an alert;

[0127] The final output is a risk status signal with a value of normal, attention, or warning. .

[0128] S5: Responds to control commands and automatically executes preset control operations;

[0129] The preset control operations performed in S5 are as follows: when a risk status signal at the warning level is received, a preset payment freeze workflow is automatically triggered to temporarily suspend the project payment function of the cost center associated with the risk status signal at the warning level; and if the payment freeze workflow fails to execute, a backup notification mechanism is automatically triggered to send alarm information to the preset supervisors.

[0130] Detailed implementation: Upon receiving a risk status signal at the warning level, the internal permission management is automatically invoked to invalidate the payment initiation permissions of all user accounts associated with the segment (cost center) that generated the warning level risk status signal. Simultaneously, to ensure effective control, the return value of the permission change operation is checked. If the operation fails due to database lockout, network interruption, or other reasons, the backup notification mechanism (in this embodiment, an SMS gateway or email service) will be activated, sending an alarm SMS containing the project name, risk level, and control failure status to the mobile phone of the project manager or financial manager, thereby achieving degraded control and ensuring that risk information is transmitted to the decision-making level regardless of circumstances.

[0131] The specific steps for performing control operations are as follows:

[0132] Input risk status signal and risk status signals Contextual information about the associated project or bid section;

[0133] Hierarchical adaptive control:

[0134] Judgment Condition 1: Judging Risk Status Signals Is the value normal?

[0135] Execution Path 1: No active control operation is performed; the controllability status is only displayed in green on the user interface.

[0136] Judgment Condition 2: Judging Risk Status Signals Is the value of [value] of interest?

[0137] Execution Path Two: Triggering a workflow that escalates alerts and enhances approval processes. This workflow specifically performs two actions:

[0138] Action 2a (Notification): Send cost concern notifications to project managers and cost managers associated with the cost center through the built-in message center or the integration interface with office software such as DingTalk and WeChat Work;

[0139] Action 2b (Process Enhancement): Automatically modify the approval template for subsequent project payment processes related to the cost center; dynamically add a project chief engineer or financial manager as an additional approval node based on the original approval nodes;

[0140] Judgment Condition 3: Judging Risk Status Signals Is the value a warning?

[0141] Execution Path 3: Workflow for triggering rigid payment circuit breaker and emergency response; the workflow specifically executes the following actions:

[0142] Action 3a (rigid control): By calling the interface of the permission management module via API, the permissions of all user roles associated with the cost center to initiate payment applications and approve payments are temporarily disabled; this operation directly stops the outflow of funds from the risk area at the system level.

[0143] Action 3b (Execution Verification): Immediately query the permission management database or call the query interface to confirm whether the permission changes of Action 3a have been successfully written and taken effect;

[0144] Action 3c (Emergency Response - Graceful Degradation): If and only if the verification result of Action 3b fails (in this embodiment, due to database connection timeout, network partition, or failure of the permission service itself), then the backup notification mechanism is immediately triggered; the notification mechanism sends a specially formatted high-priority alarm message to the mobile phone number and email address of the highest-level supervisor (in this embodiment, the unit's financial officer or general manager) preset in the system configuration by calling an independent SMS gateway HTTP API or SMTP email service interface; the message content must include: project name, cost center, current risk level (warning), control measures (payment circuit breaker) and critical status (execution failed, please intervene manually immediately!).

[0145] The final output is a series of executed control actions and / or sent notification messages.

[0146] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.< / item>

Claims

1. A data-driven dynamic closed-loop control method for water conservancy project costs, characterized in that, Includes the following steps: S1: Acquire and parse project budget data, engineering payment settlement data, and accounting subject system data, and integrate the project budget data, engineering payment settlement data, and accounting subject system data into the central data resource library according to the preset unified data model; S2: Map the project payment settlement data to the rule base of accounting subjects according to the project settlement project type, and convert the payment settlement data into structured accounting voucher data; S3: Real-time correlation between project budget data and accounting data in the central data resource library to calculate cost deviation analysis results that reflect the difference between actual costs and budget plans; S4: Based on the cost deviation analysis results, generate control instructions to indicate subsequent operations; S5: Responds to control commands and automatically executes preset control operations.

2. The data-driven dynamic closed-loop control method for water conservancy project costs according to claim 1, characterized in that: The steps for integrating project budget data, engineering cost settlement data, and accounting subject system data in the unified data model of S1 are as follows: An integrated server containing three components—data extraction, data processing, and data loading—extracts project budget data, engineering cost settlement data, and accounting subject system data from different sources and converts them into a unified XML format. Then, it loads the data into the central data resource library according to a unified data model.

3. The data-driven dynamic closed-loop control method for water conservancy project costs according to claim 2, characterized in that: In S2, data transformation maps each project settlement item type to a financial dimension that includes accounting subjects, cost centers, and budget items through a rule base.

4. The data-driven dynamic closed-loop control method for water conservancy project costs according to claim 3, characterized in that: The steps in S3 for analyzing cost deviations further include: based on the cost deviation analysis results, calculating and outputting a comprehensive controllability index to characterize the current cost control status of the project by implementing a controllability assessment model through preliminary calculation.

5. The data-driven dynamic closed-loop control method for water conservancy project costs according to claim 4, characterized in that: The steps for generating the comprehensive controllability index are as follows: The cost deviation rate, investment rate deviation, and cost deviation momentum in the cost deviation analysis results are normalized to obtain three standardized risk factors: cost deviation risk factor, investment rate risk factor, and momentum risk factor. The cost deviation risk factor, investment rate risk factor, and momentum risk factor are integrated into a single comprehensive risk score. Based on the comprehensive risk score, the final comprehensive controllability index is calculated.

6. The data-driven dynamic closed-loop control method for water conservancy project costs according to claim 5, characterized in that: The steps for generating control commands are as follows: Input the comprehensive controllability index, which is used to calculate the relevant parameters of the dynamic threshold, including the baseline first-level threshold, the baseline second-level threshold, and the project stage adjustment coefficient; The current dynamic primary threshold and dynamic secondary threshold are calculated based on the baseline primary threshold, the baseline secondary threshold, and the project stage adjustment coefficient.

7. The data-driven dynamic closed-loop control method for water conservancy project costs according to claim 6, characterized in that: The steps for logical judgment and signal generation of control commands are as follows: Judgment condition 1: Determine whether the comprehensive controllability index is greater than the dynamic first-level threshold; Execution path one: If condition one is true, generate a risk status signal and set its value to normal; Judgment Condition 2: If Condition 1 is false, then continue to judge whether the comprehensive controllability index is greater than the dynamic secondary threshold; Execution path two: If condition two is true, then generate a risk status signal and set its value to "attention"; Execution path three: If condition two is also false, then generate a risk status signal and set its value to warning; The final output is a risk status signal with a value of normal, attention, or warning.

8. The data-driven dynamic closed-loop control method for water conservancy project costs according to claim 1, characterized in that: The preset control operations performed in S5 are as follows: when a risk status signal at the warning level is received, a preset payment freeze workflow is automatically triggered to temporarily suspend the project payment function of the cost center associated with the risk status signal at the warning level; and if the payment freeze workflow fails to execute, a backup notification mechanism is automatically triggered to send alarm information to the preset supervisors.

9. The data-driven dynamic closed-loop control method for water conservancy project costs according to claim 8, characterized in that: The specific steps for performing control operations are as follows: Input the risk status signal, and the context information of the project or section associated with the risk status signal; Hierarchical adaptive control: Judgment condition 1: Determine whether the value of the risk status signal is normal; Execution Path 1: No active control operation is performed; the controllability status is only displayed in green on the user interface. Judgment Condition 2: Determine whether the value of the risk status signal is of concern; Execution Path Two: Triggering a workflow that escalates alerts and enhances approval processes. This workflow specifically performs two actions: Action 2a: Send cost concern notifications to project managers and cost managers associated with the cost center through the built-in message center or the integration interface with office software such as DingTalk and WeChat. Action 2b: Automatically modify the approval template for subsequent project payment processes related to the cost center; dynamically add a project chief engineer or financial officer as an additional approval node based on the original approval nodes; Judgment Condition 3: Determine whether the value of the risk status signal is a warning; Execution Path 3: Workflow that triggers rigid payment circuit breakers and emergency responses.

10. The data-driven dynamic closed-loop control method for water conservancy project costs according to claim 9, characterized in that: The workflow specifically performs the following actions: Action 3a: By calling the API of the permission management module, the permissions to initiate and approve payments for all user roles associated with the cost center are temporarily disabled; this operation directly stops the outflow of funds from the risk area at the system level. Action 3b: Immediately query the permission management database or call the query interface to confirm whether the permission change in Action 3a has been successfully written and taken effect; Action 3c: If and only if the verification result of Action 3b is a failure, then the backup notification mechanism shall be triggered immediately. The notification mechanism sends a specially formatted high-priority alarm message to the mobile phone number and email address of the highest-level supervisor pre-configured in the system by calling an independent SMS gateway HTTP API or SMTP email service interface. The message content must include: project name, cost center, current risk level, control measures, and critical status.

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