An Optimization Method for a Financial Management System for Construction Personnel

Abstract

This invention discloses an optimization method for a financial management system for construction workers, relating to the field of financial management technology. After identifying financial anomalies, a fund utilization stability index is constructed based on expenditure change curves. Simultaneously, a resource optimization index is constructed based on resource utilization. The fund utilization stability index and the resource optimization index are jointly input into an intelligent optimization model trained on historical data. This model automatically calculates a financial coordination index. If the financial coordination index is lower than a preset standard, it automatically determines that financial management and resource allocation have not reached an optimal state. Based on the real-time calculated financial coordination index and its fluctuations over different time periods, the model autonomously selects and executes a series of optimization strategies, including intelligent budget adjustment and expenditure control, dynamic optimization of working hours and resources, and real-time generation of financial health reports and intelligent reminders. This optimization method, through an intelligent optimization model, achieves timely identification of financial anomalies and resource optimization, effectively improving financial management efficiency.

Description

Technical Field

[0001] This invention relates to the field of financial management technology, and specifically to an optimization method for a financial management system for construction workers. Background Technology

[0002] In engineering construction projects, construction workers are the core force in carrying out specific tasks and are responsible for ensuring the smooth progress of the project. The financial management system is an important tool for managing cash flow, cost control, and profit analysis. As the complexity of engineering projects increases, the original financial management system may face problems such as low efficiency and inaccurate fund management. Therefore, it needs to be optimized to improve its processing capacity and ability to cope with complex situations.

[0003] The existing technology has the following drawbacks:

[0004] Existing management systems typically cannot monitor construction personnel's resource usage and financial expenditures in real time, resulting in an inability to adjust budgets and resource allocation in a timely manner. This affects construction progress and cost control, and makes it difficult to comprehensively consider expenditure growth rates, resource utilization efficiency, and overall financial situation. Consequently, it is impossible to take timely optimization strategies when problems arise, making it difficult to effectively curb financial deviations and resource waste during construction, ultimately affecting the overall project's financial management and resource utilization efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide an optimization method for the financial management system of construction personnel. Through an intelligent optimization model, it enables timely identification of financial anomalies and resource optimization, thereby effectively improving the efficiency of financial management.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an optimization method for a financial management system for construction workers, the optimization method comprising the following steps:

[0007] S1: During the construction process, relevant financial data and resource usage data of construction personnel are collected. After the data collection is completed, the expenditure change curve is automatically analyzed, the expenditure growth rate index is calculated, and the resource utilization trajectory is monitored in real time to extract performance indicators. If any indicator exceeds the predetermined budget range or the resource utilization threshold, it is automatically judged as a financial anomaly.

[0008] S2: After identifying financial anomalies, a fund usage stability index is constructed based on the expenditure change curve. At the same time, a resource optimization index is constructed based on resource utilization. The fund usage stability index and the resource optimization index are jointly input into an intelligent optimization model trained on historical data to automatically calculate the financial coordination index. If the financial coordination index is lower than the preset standard, it is automatically determined that financial management and resource allocation have not reached the optimal state.

[0009] S3: Based on the real-time calculated financial coordination index and its fluctuations over different time periods, it will autonomously select and execute a series of optimization strategies, including intelligent budget adjustment and expenditure control, dynamic optimization of working hours and resources, and generation of real-time financial health reports and intelligent reminders.

[0010] In a preferred embodiment, the stability index of fund utilization and the resource optimization index are jointly input into an intelligent optimization model trained on historical data to automatically calculate the financial coordination index. If the financial coordination index is lower than a preset standard, it is automatically determined that financial management and resource allocation have not reached the optimal state, including the following steps:

[0011] The input variables are the capital utilization stability index and the resource optimization index;

[0012] The model training data comes from the financial and resource data of similar historical engineering projects, including past fund usage stability index and resource optimization index, as well as corresponding resource conflict event records, project delays, and overrun ratios;

[0013] The model outputs a financial coordination index, with a value ranging from 0 to 1, which reflects the degree of coordination and matching between the current financial expenditure arrangements and the resource utilization status.

[0014] The financial coordination index output by the model is compared with a pre-set index threshold:

[0015] If the financial coordination index value is greater than or equal to the index threshold, it indicates that the current financial expenditure and resource utilization have reached a state of coordination and balance, and the optimization process will not be triggered for the time being, but monitoring will continue.

[0016] If the financial coordination index value is less than the index threshold, it will be automatically determined that the current financial management and resource allocation have failed to achieve optimal coordination.

[0017] In a preferred embodiment, after identifying financial anomalies, a fund utilization stability index is constructed based on the expenditure change curve, including the following steps:

[0018] The collected expenditure time series data is smoothed. Based on the smoothed expenditure series, the expenditure change amplitude between adjacent periods is calculated, and the mean and standard deviation of the fluctuation amplitude within the time window are statistically analyzed to measure the intensity and dispersion of expenditure fluctuations.

[0019] Further analysis is needed to determine whether the overall trend of the expenditure sequence conforms to the expected pattern. If the expenditure curve shows violent fluctuations, sharp increases or decreases, or multiple abnormal peaks in a short period of time, it is judged to be unstable.

[0020] Based on the above characteristics of volatility, standard deviation, and trend consistency, a weighted comprehensive scoring mechanism is adopted to output a fund utilization stability index ranging from 0 to 1.

[0021] The Financial Stability Index (FSI) is calculated based on smoothing time-series expenditure data. First, the original expenditure data (such as various financial expenditures statistically analyzed weekly or monthly) is subjected to a moving average or low-pass filter to eliminate short-term noise, resulting in a smoothed expenditure series. Then, the magnitude of expenditure changes between adjacent periods (such as two consecutive weeks or two months) is calculated (this can be the absolute difference or relative growth rate). Finally, the mean and standard deviation of the volatility within a set time window (such as the past four weeks or three months) are statistically analyzed to quantify the intensity and dispersion of expenditure fluctuations. The system analyzes whether the overall trend of the expenditure sequence conforms to the expected pattern (such as steady growth or reasonable fluctuations). If the expenditure curve shows violent oscillations, sharp increases or decreases, or multiple abnormal peaks in a short period of time, it is judged as having poor trend consistency. Finally, based on characteristics such as volatility, standard deviation, and trend consistency, the system adopts a weighted comprehensive scoring mechanism (e.g., volatility accounts for 40% of the weight, standard deviation accounts for 30%, and trend consistency accounts for 30%). After normalizing each feature value, the system performs a weighted summation and outputs a fund utilization stability index (FSI) with a value range of 0 to 1. The higher the value, the more stable and controllable the fund expenditure is, and the better the stability is; conversely, the lower the value, the worse the stability is.

[0022] In a preferred embodiment, constructing a resource optimization index based on resource utilization includes the following steps:

[0023] Multiple performance indicators are automatically obtained from resource utilization data, including working hours utilization rate, equipment operation rate, task completion rate, resource balance and idle rate;

[0024] A comprehensive analysis of multiple performance indicators is conducted, and a multi-factor weighted scoring method is used to output an index that reflects the overall optimization level of resource utilization—the resource optimization index, whose value ranges from 0 to 1.

[0025] The Resource Optimization Index (ROI) is calculated based on multiple key performance indicators automatically extracted from resource utilization data, including work hour utilization rate (the ratio of actual effective work hours to planned work hours), equipment operation rate (the ratio of actual equipment operating time to available time), task completion rate (the ratio of completed work to planned work), resource balance (the degree of balance in input among different types of work or equipment), and idle rate (the ratio of unused resources to total resources). The system comprehensively analyzes these indicators and adopts a multi-factor weighted scoring mechanism (e.g., work hour utilization rate and equipment operation rate each account for 30% of the weight, task completion rate accounts for 20%, resource balance accounts for 10%, and idle rate is included as a negative indicator, accounting for 10%). After normalizing each indicator, it is weighted and summed according to the predetermined weights, and finally outputs a Resource Optimization Index (ROI) with a value range of 0 to 1. The higher the value, the more fully the resources are utilized, the more reasonable the allocation, the more efficient the scheduling, and the better the overall resource optimization level. Conversely, there is obvious inefficiency or waste.

[0026] In a preferred embodiment, after data collection is completed, the expenditure change curve is automatically analyzed, and the rate of change of expenditure growth is calculated, including the following steps:

[0027] The original expenditure time series data is denoised and aggregated by cumulative or mean at fixed periods to generate a smooth expenditure trend series.

[0028] For each period's expenditure amount, calculate the difference between it and the previous period's amount, and then divide it by the previous period's amount to obtain the expenditure growth rate, which is used to reflect the increase or decrease in expenditure in the current period relative to the previous period.

[0029] Using sliding window or statistical distribution methods, statistical analysis is performed on the expenditure growth rate series to identify outliers that deviate from the normal fluctuation range;

[0030] The cumulative expenditure amount or single expenditure amount for each period is compared with the budget limit for the corresponding period or category in the project budget table. If the actual expenditure exceeds a certain percentage of the budget value, a financial overspending warning is triggered.

[0031] In a preferred embodiment, resource utilization trajectory is monitored in real time, performance indicators are extracted, and if any indicator exceeds the predetermined budget range or resource utilization threshold, it is automatically identified as a financial anomaly, including the following steps:

[0032] Compare performance indicators with pre-set resource utilization thresholds;

[0033] If the maximum / minimum fluctuation difference of working hours for a certain job exceeds the set threshold, it indicates that there is uneven distribution of working hours or idle time.

[0034] If the equipment utilization rate is below a certain lower limit, it indicates that there is equipment redundancy or unreasonable scheduling.

[0035] If the schedule deviation rate exceeds ±10%, it indicates that it affects the connection of subsequent processes and the overall project duration.

[0036] If any financial expenditure indicator exceeds the budget range, or any resource utilization performance indicator exceeds the corresponding threshold, the project will be automatically identified as having financial anomalies.

[0037] In a preferred embodiment, based on the real-time calculated financial coordination index and its fluctuations over different time periods, a series of optimization strategies will be autonomously selected and executed, including the following steps:

[0038] By dynamically analyzing the actual occurrence of each expenditure category, the progress of budget execution, and the status of financial coordination, the budget allocation plan for various financial expenditures is automatically optimized to control the pace of expenditure.

[0039] Based on real-time collected work performance data and financial coordination status, automatically optimize staffing hours and equipment usage plans;

[0040] Based on multidimensional data on the current financial coordination index, expenditure and resource status, a visualized and structured financial health report is automatically generated. Through an intelligent message push and early warning mechanism, potential financial risks and resource issues are communicated to project management.

[0041] In a preferred embodiment, by dynamically analyzing the actual occurrence of each expenditure category, budget execution progress, and financial coordination status, the budget allocation scheme for various financial expenditures is automatically optimized, and the expenditure pace is controlled. The processing logic is as follows: the cumulative amount and budget of each expenditure category are summarized in real time, the budget execution rate and expenditure deviation rate are calculated, and their changing trends are analyzed in terms of time. If the current financial coordination index is low, the expenditure change curve and stability index are further combined to identify the expenditure categories that cause financial incoordination.

[0042] In a preferred embodiment, the relevant financial data of the construction personnel includes personnel wages, material costs, and equipment depreciation expenditures. The trend of these data changes over time is tracked in real time to generate an expenditure change curve.

[0043] In a preferred embodiment, the resource usage data of the construction personnel includes work hour allocation, equipment utilization rate, work progress, monitoring fluctuations and changes in resource utilization, and plotting resource efficiency trajectories.

[0044] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0045] 1. This invention, by introducing an automated data acquisition and intelligent analysis mechanism, achieves real-time monitoring and dynamic management of financial data and resource usage. By accurately calculating expenditure change rates and resource utilization trajectories, the system can promptly detect and determine financial anomalies, avoiding problems caused by human intervention and data processing delays.

[0046] 2. This invention, by constructing a fund utilization stability index and a resource optimization index, and combining them with an intelligent optimization model, can automatically identify and calculate a financial coordination index when financial deviations occur, thereby scientifically assessing the rationality of financial management and resource allocation. This method enables refined budget adjustments, resource optimization, and real-time financial report generation. Through an intelligent reminder mechanism, it ensures that potential problems can be identified and resolved promptly during construction, significantly improving financial management efficiency and reducing the risk of resource waste and cost overruns. Ultimately, the entire system achieves intelligent management of construction projects without human intervention, optimizing financial management processes and improving resource utilization efficiency. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0048] Figure 1 This is a flowchart of the optimization method of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0050] Example 1: Please refer to Figure 1 As shown in the figure, this embodiment provides an optimization method for a financial management system for construction workers. The optimization method includes the following steps:

[0051] S1: During the construction process, relevant financial and resource usage data of construction personnel are automatically collected, mainly divided into two categories:

[0052] Financial expenditures and cash flow: This includes expenditures such as personnel salaries, material costs, and equipment depreciation. It tracks the trends of these data changes over time in real time and generates expenditure change curves.

[0053] Resource allocation and work performance: including work hour allocation, equipment utilization, work progress, etc., monitoring fluctuations and changes in resource utilization, and mapping resource efficiency trajectories.

[0054] After data collection is complete, the system will automatically analyze the expenditure change curve, calculate the rate of change of expenditure growth (e.g., the gradient of expenditure change), monitor the resource utilization trajectory in real time, and extract key performance indicators (e.g., the maximum / minimum fluctuation difference of working hours). If any indicator exceeds the predetermined budget range or resource utilization threshold, the system will automatically determine it as a financial anomaly and proceed to the next processing step.

[0055] S2: Upon identifying financial anomalies, a fund utilization stability index is constructed based on the expenditure change curve to assess the stability of current financial expenditures. Simultaneously, a resource optimization index is constructed based on resource utilization to reflect the effective allocation and efficient use of construction resources. The fund utilization stability index and resource optimization index are jointly input into an intelligent optimization model trained on historical data to automatically calculate a financial coordination index. This index reflects the coordination and balance between the current financial situation and the utilization of construction resources. If the financial coordination index falls below a preset standard, the system will automatically determine that financial management and resource allocation have not reached an optimal state, thereby triggering subsequent optimization and adjustment strategies.

[0056] S3: Based on the real-time calculated financial coordination index and its fluctuations over different time periods, the system will autonomously select and execute a series of optimization strategies, including:

[0057] Intelligent budget adjustment and expenditure control: By automatically analyzing the current financial situation, the system will dynamically adjust various expenditure budgets, optimize financial allocation, and avoid overspending or waste of resources.

[0058] Dynamic optimization of working hours and resources: Based on work performance data, the system automatically adjusts personnel working hours and equipment usage plans to improve construction efficiency and maximize resource utilization.

[0059] Real-time financial health report generation and intelligent reminders: The system will automatically generate financial health reports and promptly convey potential financial problems or risks to management through an intelligent reminder mechanism, avoiding excessive reliance on manual intervention.

[0060] This application introduces an automated data collection and intelligent analysis mechanism to achieve real-time monitoring and dynamic management of financial data and resource usage. By accurately calculating expenditure change rates and resource utilization trajectories, the system can promptly detect and determine financial anomalies, avoiding problems caused by human intervention and data processing delays. By constructing a fund utilization stability index and a resource optimization index, and combining them with an intelligent optimization model, the system can automatically identify and calculate a financial coordination index when financial deviations occur, thereby scientifically assessing the rationality of financial management and resource allocation. This method enables refined budget adjustments, resource optimization, and real-time financial report generation. Through an intelligent reminder mechanism, it ensures that potential problems can be identified and resolved promptly during construction, significantly improving financial management efficiency and reducing the risk of resource waste and cost overruns. Ultimately, the entire system achieves intelligent management of construction projects without human intervention, optimizing financial management processes and improving resource utilization efficiency.

[0061] Example 2

[0062] S1: During the construction process, relevant financial and resource usage data of construction personnel are collected. After the data collection is completed, the expenditure change curve is automatically analyzed, the expenditure growth rate index is calculated, and the resource utilization trajectory is monitored in real time to extract performance indicators. If any indicator exceeds the predetermined budget range or the resource utilization threshold, it is automatically judged as a financial anomaly.

[0063] During the execution phase of engineering construction projects, to achieve precise control over the project's financial health and resource utilization efficiency, the system first initiates a full-process, multi-dimensional, automated collection and structured processing of financial expenditure data and resource usage data directly or indirectly related to construction personnel. The collected data is divided into two core categories according to business attributes and analysis objectives: financial expenditure and cash flow data, and resource allocation and work performance data. These reflect the project's financial operation status and resource allocation efficiency, respectively, providing a high-quality data foundation for subsequent financial trend analysis, anomaly identification, and decision support.

[0064] Financial expenditure and cash flow data:

[0065] This category of data focuses on direct and indirect financial costs incurred during project execution, specifically covering, but not limited to, the following key expenditure types:

[0066] Personnel wages: Salary expenditures for construction personnel are calculated by position, type of work, work team, time period, etc., including basic salary, performance bonus, overtime pay, allowances and other sub-items;

[0067] Material costs: This includes the procurement costs of main materials, auxiliary materials, and consumables required for construction, recorded in detail by material category, supplier, location of use, and time of issuance.

[0068] Equipment depreciation and maintenance costs: including depreciation of fixed assets such as construction machinery, transportation equipment, and power tools, routine maintenance, fuel consumption, and rental expenses;

[0069] Other financial expenditures: such as temporary facility construction fees, water and electricity costs, outsourced labor costs, safety and protection expenditures, and other auxiliary fund flows.

[0070] The system connects to multiple heterogeneous data sources, including the project's ERP system, financial accounting module, material requisition platform, attendance and payroll system, and equipment IoT terminals. Through methods such as timed polling, event triggering, and interface synchronization, it achieves real-time or near real-time collection and structured storage of the aforementioned financial data, ensuring the accuracy, consistency, and integrity of the data.

[0071] Based on data collection, the system further performs time series modeling on various expenditure items. Specifically, for each type of expenditure (such as personnel salaries and material costs), it aggregates cumulative or average data according to time units (day / week / month) to construct a corresponding expenditure time series dataset, and generates an expenditure change curve based on this dataset. This curve, with time on the horizontal axis and cumulative or current expenditure amount on the vertical axis, visually displays the fluctuation trend of various financial expenditures as the project progresses, providing a visual and quantitative basis for subsequent trend analysis and anomaly detection.

[0072] Resource allocation and work performance data:

[0073] This type of data focuses on depicting the allocation status of human and equipment resources during the construction process and their actual output efficiency, mainly including:

[0074] Work time allocation data: Records the actual working time of each type of work, team, and individual on a specific work surface or process, including normal working hours, overtime working hours, and idle working hours, usually counted in workdays or working hours.

[0075] Equipment utilization rate: refers to the ratio of the actual operating time of construction machinery and equipment to the theoretical available time within a specific period. It reflects whether the equipment is operating efficiently and avoids the phenomenon of "equipment idle" or "overuse".

[0076] Work progress data: Based on the construction plan and actual completion status, the progress percentage, milestone achievement status, and delay or advance status of each sub-item of the project are statistically analyzed, usually measured by the amount of work, the completion status of the process, or milestone nodes.

[0077] Resource scheduling records include personnel schedules, equipment dispatch records, task assignment sheets, etc., which are used to reconstruct the decision-making process and execution path of resource scheduling.

[0078] This type of data is also collected by integrating data sources such as on-site attendance systems, construction progress management systems, equipment IoT sensors (such as GPS positioning and operational status monitoring), and task management platforms to form time series and status snapshot data of resource utilization, and further plot the resource efficiency trajectory. This trajectory uses time as the horizontal axis and key resource utilization indicators (such as man-hour input, equipment operating rate, and task completion rate) as the vertical axis to dynamically display the actual performance of resource scheduling and utilization, providing process-level data support for resource optimization analysis.

[0079] After completing the collection and preliminary structuring of the above two types of data, the system enters the automated data analysis and anomaly detection phase. The core objective of this phase is to identify, through quantitative analysis, whether there are any deviations from expectations, exceeding controllable limits, or potential risks in the current project's financial expenditures and resource utilization; that is, to determine whether financial anomalies have occurred, so as to trigger subsequent optimization processes in a timely manner. Firstly, for the expenditure change curves (i.e., time series data of various financial expenditures changing over time), trend feature extraction and change rate calculation are performed to quantitatively assess the growth trend and fluctuation characteristics of financial expenditures.

[0080] The original expenditure time series data is denoised (e.g., by moving average or low-pass filtering) and aggregated by cumulative or mean at fixed periods (e.g., weekly or monthly) to generate a smooth expenditure trend series.

[0081] For each period's expenditure amount, the system calculates the difference between it and the previous period's amount, and further divides it by the previous period's amount to obtain the expenditure growth rate (or rate of change), which reflects the increase or decrease in expenditure in the current period relative to the previous period. The system compares the current period's expenditure with the previous period's expenditure sequentially. If the current expenditure is significantly higher than the previous period's (e.g., the growth rate exceeds a certain multiple of the historical average for the same period, or exceeds a preset growth rate threshold, such as 20%), it is considered rapid expenditure growth; conversely, an abnormal decrease may reflect the risk of a broken cash flow or project stagnation.

[0082] The system employs sliding window or statistical distribution methods (such as the standard deviation method and box plot method) to perform statistical analysis on the expenditure growth rate series, identifying "outliers" or "abrupt intervals" that significantly deviate from the normal fluctuation range. For example, if the expenditure growth rate of a certain period exceeds the historical period's average growth rate plus twice the standard deviation, it is determined to be abnormal growth.

[0083] The cumulative expenditure amount or single expenditure amount for each period is compared with the budget limit for the corresponding period or category in the project budget table. If the actual expenditure exceeds the budget value by a certain percentage (such as 10% or as set according to management requirements), a financial overspending warning is triggered.

[0084] At the same time, the system monitors resource efficiency trajectories (i.e., time series or status data of key indicators such as working hours, equipment utilization, and progress) in real time and extracts key performance indicators (KPIs) to assess whether resources are rationally allocated and efficiently utilized.

[0085] Automatically calculate multiple core performance indicators from resource data, including but not limited to:

[0086] Working hours fluctuation difference: The difference between the maximum and minimum working hours of a certain job or work group within a specific time period is used to reflect the balance of working hours distribution.

[0087] Average and fluctuation range of equipment utilization: Calculate the average utilization of equipment over multiple time periods and identify the difference between its lowest and highest utilization rates;

[0088] Schedule Deviation Rate: Compare the actual completed work with the planned work to calculate the percentage of time ahead or behind schedule;

[0089] Resource idle rate: The percentage of working hours and equipment time that are not assigned tasks or are running inefficiently out of the total available time.

[0090] Compare the above key performance indicators with pre-set resource utilization thresholds. For example:

[0091] If the maximum / minimum fluctuation difference of working hours for a certain job exceeds the set threshold (e.g., 40 hours), there may be uneven distribution of working hours or idle time. If the equipment utilization rate is consistently below a certain lower limit (e.g., 30%), there may be equipment redundancy or unreasonable scheduling. If the progress deviation rate exceeds ±10%, it may affect the connection of subsequent processes and the overall project schedule.

[0092] The analysis results of the above financial expenditures and resource utilization are jointly judged: if any financial expenditure indicator (such as expenditure growth rate, overspending amount) exceeds the budget range, or any resource utilization KPI (such as working hour fluctuation difference, equipment utilization rate, schedule deviation) exceeds the corresponding threshold, the current project is automatically judged to have financial anomalies, and immediately transferred to the subsequent financial stability and resource coordination assessment and optimization strategy generation process to initiate in-depth analysis and intelligent control mechanism.

[0093] S2: After identifying financial anomalies, a fund usage stability index is constructed based on the expenditure change curve. At the same time, a resource optimization index is constructed based on resource utilization. The fund usage stability index and the resource optimization index are jointly input into an intelligent optimization model trained on historical data to automatically calculate the financial coordination index. If the financial coordination index is lower than the preset standard, it is automatically determined that financial management and resource allocation have not reached the optimal state.

[0094] When the system determines that there are financial anomalies in the current construction project (e.g., a significant increase in a certain type of expenditure in a short period of time, persistently low resource utilization, serious imbalance in work hour allocation, and deviation from the schedule) through real-time collection, trend analysis, key indicator extraction, and threshold comparison of financial expenditure and resource utilization data in the S1 stage, it enters this stage—the comprehensive assessment and coordination analysis stage of financial and resource status.

[0095] The core objective of this phase is to quantitatively assess the current project's financial expenditure and resource utilization status from three dimensions: stability, efficiency, and synergy. Then, through intelligent models, the degree of coordination and matching between the two will be calculated to provide a basis for decision-making in generating subsequent optimization strategies.

[0096] Specifically, the system will sequentially construct two core evaluation indices—the fund utilization stability index and the resource optimization index—and use them as input variables to pass to an intelligent optimization model trained on historical data. This model will then output a comprehensive evaluation index—the financial coordination index. This index quantitatively measures whether the current project has achieved an optimal match between financial expenditure arrangements and construction resource scheduling. If this index falls below a pre-set threshold, the system determines that the current financial management and resource allocation mechanisms have failed to achieve efficient coordination, and automatically triggers subsequent optimization and adjustment strategies (entering S3).

[0097] After identifying financial anomalies, the system first focuses on the time-series characteristics of financial expenditure data, especially the trend and fluctuation characteristics of expenditure amounts over time. Based on this, it constructs a quantitative indicator for assessing the stability of financial expenditures—the Financial-Stability-Index (FSI). Through comprehensive analysis of the fluctuation amplitude, rate of change, and trend smoothness of the expenditure change curve, the system quantitatively assesses the "stability" and "controllability" of current financial expenditures, thereby determining whether fund usage is within a reasonable fluctuation range or whether there are abnormal fluctuations, overspending risks, or unstable conditions such as tight cash flow.

[0098] The collected expenditure time series data (such as personnel wages, material costs, equipment costs, etc. aggregated by week / month) is smoothed using common methods including moving averages or low-pass filtering to eliminate short-term noise interference and extract the main trend components. Based on the smoothed expenditure series, the system calculates the expenditure change amplitude (absolute difference or relative growth rate) between adjacent periods (such as week to week, month to month) and statistically analyzes the mean and standard deviation of the fluctuation amplitude within a certain time window (such as the last 4 weeks or the last 3 months) to measure the intensity and dispersion of expenditure fluctuations. Further analysis is conducted to determine whether the overall trend of the expenditure series conforms to the expected pattern (such as linear growth, step-like growth, or stable maintenance). If the expenditure curve exhibits violent oscillations, sharp increases or decreases, or multiple abnormal peaks in a short period, it is judged to have poor stability.

[0099] Based on the aforementioned characteristics such as volatility, standard deviation, and trend consistency, a capital utilization stability index (FSI) is ultimately output by setting multi-dimensional evaluation rules (such as whether the volatility exceeds a certain multiple of the historical average for the same period, whether there is a continuous abnormal growth cycle, etc.) and adopting a weighted comprehensive scoring mechanism.

[0100] If expenditures fluctuate little, trend smoothly, and conform to the budget schedule, the fund utilization stability index is close to 1, indicating highly stable fund utilization. Conversely, if expenditures fluctuate frequently, are large in magnitude, and deviate from the budget, the fund utilization stability index is close to 0, indicating extremely unstable fund utilization. This index provides key input information for subsequent models regarding whether financial expenditures are under control and stable.

[0101] In conjunction with the assessment of financial stability, a quantitative indicator—the Resource-Optimization-Index (ROI)—was constructed based on resource allocation and work performance data during the construction process to measure resource utilization efficiency and allocation rationality. This index aims to reflect whether the current project's scheduling of key resources such as manpower and equipment is scientific, whether their utilization is sufficient, and whether there are issues such as idleness, inefficiency, or over-investment, thereby providing data support for judging whether resources have been optimally allocated.

[0102] Several core performance indicators are automatically calculated from resource utilization data, including:

[0103] Work time utilization rate: The ratio of actual effective working hours to planned working hours;

[0104] Equipment uptime: The ratio of actual equipment uptime to available time;

[0105] Task completion rate: The proportion of completed work or procedures to the planned work volume;

[0106] Resource balance: The degree of difference in working hours or usage frequency among different types of work and equipment;

[0107] Idle rate: The percentage of resources that are not fully utilized.

[0108] A comprehensive analysis of the above KPIs identifies bottlenecks and inefficiencies in resource utilization. For example, if a certain type of equipment is consistently operating at a low rate, or if the utilization rate of a particular job is significantly below average, it indicates resource misallocation or unreasonable scheduling. Further analysis, combined with historical benchmark data from similar projects (or industry averages), assesses the gap between the current resource utilization status and the ideal state, determining whether there is room for efficiency improvement through scheduling optimization, task reallocation, or equipment relocation.

[0109] Based on the above analysis, a multi-factor weighted scoring method is used to output an index reflecting the overall optimization level of resource utilization—the Resource Optimization Index (ROI), whose value typically ranges from 0 to 1 (or 0 to 100). If the utilization rate of working hours is high, equipment is fully operational, tasks are progressing smoothly, and resource allocation is balanced, then the ROI is close to 1, indicating highly optimized resource utilization; conversely, if there are a large number of idle, inefficient, or unbalanced phenomena, then the ROI is close to 0, indicating that resource utilization urgently needs optimization. This index provides the intelligent model with an important input dimension regarding "whether resources are used efficiently and rationally."

[0110] After obtaining the two core evaluation indicators, Funds Stability Index (FSI) and Resource Optimization Index (ROI), they are used as input variables and passed to an intelligent optimization model (such as an integrated decision model) that has been trained with historical project data.

[0111] The core function of this model is to learn and simulate the synergistic effect and balance between overall project financial management and resource allocation under different combinations of financial stability and resource optimization levels, and then output a quantitative indicator for comprehensively evaluating the degree of matching between the two—the Financial-Resource-Coordination-Index (FRCI).

[0112] Model input and processing logic description:

[0113] Input variables: Funds Utilization Stability Index (FSI), Resource Optimization Index (ROI);

[0114] Model training data: derived from financial and resource data of similar historical engineering projects, including past FSI, ROI and corresponding resource conflict event records, schedule delays, cost overruns and other labeled data;

[0115] Model output: Financial Coordination Index (FRCI), typically ranging from 0 to 1 (or standardized to a percentage), which reflects the degree of coordination and matching between current financial expenditure arrangements and resource utilization status.

[0116] Processing logic description: If both the fund utilization stability index and the resource optimization index are at a high level (e.g., both greater than 0.8), and historical data shows that the project is financially healthy and resource allocation is smooth, then the model outputs a high financial coordination index (close to 1), indicating a high degree of coordination between finance and resources; conversely, if one or both are low (e.g., FSI < 0.4 or ROI < 0.5), the model determines that the coordination between the two is poor, and the output financial coordination index value is low (e.g., < 0.6), indicating that there is a need for optimization.

[0117] Compare the Financial Harmonization Index (FRCI) output by the model with a pre-set index threshold (such as 0.7 or 0.75):

[0118] If the financial coordination index value is greater than or equal to the index threshold, it indicates that the current financial expenditure and resource utilization have basically reached a state of coordination and balance, and the optimization process will not be triggered for the time being, but monitoring will continue.

[0119] If the financial coordination index value is less than the index threshold, it will be automatically determined that the current financial management and resource allocation have failed to achieve optimal coordination, and there are problems such as unreasonable fund allocation, inefficient resource scheduling, and mismatch between the two. Then, the subsequent S3 optimization and adjustment strategy execution process will be automatically triggered to achieve dynamic optimization and rebalancing of finance and resources through measures such as intelligent budget adjustment, replanning of working hours and equipment, and real-time risk warning.

[0120] S3: Based on the real-time calculated financial coordination index and its fluctuations over different time periods, it will autonomously select and execute a series of optimization strategies, including intelligent budget adjustment and expenditure control, dynamic optimization of working hours and resources, and generation of real-time financial health reports and intelligent reminders.

[0121] In phase S2, the system constructs and calculates the Financial-Resource-Coordination-Index (FRCI) to quantitatively assess the synergy and balance between financial expenditure arrangements and construction resource utilization in the current project. If the index is lower than a pre-set standard threshold (such as 0.7 or 0.75), the system determines that the current financial management and resource allocation mechanism has failed to achieve optimal synergy, and there are potential problems such as unreasonable fund allocation, inefficient resource utilization, and imbalance between the two. This leads to the next phase—the autonomous selection and execution of intelligent optimization strategies.

[0122] The optimization strategies executed by the system mainly include the following three categories. Each strategy is based on data-driven logic and rule engine mechanism, combined with real-time data feedback and historical control experience, to achieve full-process automation from "problem identification" to "strategy execution", avoiding the inefficiency and lag of traditional reliance on human experience judgment and manual intervention.

[0123] I. Intelligent Budget Adjustment and Expenditure Control:

[0124] By dynamically analyzing the actual occurrence of each expenditure category, budget execution progress, and financial coordination status, the system automatically optimizes the budget allocation scheme for various financial expenditures, controls the pace of expenditure, prevents the risk of overspending and waste of resources, and ensures that funds are used more rationally, efficiently, and controllably. The processing logic is as follows:

[0125] The system provides real-time aggregated data on the cumulative amounts and budgeted amounts for each major expenditure category (such as personnel salaries, material costs, equipment depreciation, and outsourcing fees), calculates the budget execution rate (actual expenditure / budgeted amount) and expenditure deviation rate ((actual - budgeted) / budgeted), and analyzes their trends over time (weekly / monthly). If the current financial coordination index is low, the system further combines expenditure change curves and the Financial Stability Index (FSI) to identify the main expenditure categories causing financial inconsistencies. For example, if the growth rate of material costs is too high and fluctuates significantly, it may be the main source of current financial pressure; if the proportion of personnel salary expenditure remains high, there may be problems with over-allocation of human resources or an unreasonable salary structure. Based on the above analysis, the system adopts a priority control and flexible allocation mechanism to dynamically adjust the remaining budget for different expenditure categories.

[0126] For expenditure categories with high growth, high deviation, and low efficiency (such as abnormally high material costs or equipment rental fees in the short term), the system automatically suggests or implements control measures such as budget reduction, delayed payment, and phased procurement. For expenditure categories on the critical path and with strong coordination (such as labor costs or core equipment usage fees that directly affect construction progress), the system can appropriately guarantee their funding within the overall budget framework to avoid schedule risks caused by excessive reduction. The optimized budget allocation plan is automatically synchronized to the finance module, procurement module, or project execution system. By setting expenditure warning lines, automatically intercepting over-budget applications, and dynamically adjusting payment plans, the system achieves automated and intelligent expenditure control.

[0127] II. Dynamic Optimization of Working Hours and Resources:

[0128] Based on real-time collected work performance data (such as work hours, equipment utilization, and task completion rate) and financial coordination status, the system automatically optimizes personnel work hour arrangements and equipment usage plans to improve construction efficiency, reduce ineffective input, and maximize and optimize resource utilization. The processing logic is as follows:

[0129] The system statistically analyzes current personnel work hour allocation (e.g., comparison of actual daily / weekly work hours with planned work hours for each job type) and equipment operating status (e.g., actual equipment runtime, standby time, failure rate, etc.) to identify inefficient processes or areas of resource redundancy. For example, if the work hours for a certain job type far exceed the plan but output does not increase accordingly, or if a certain type of equipment is in a state of low operating rate for a long time. If the financial coordination index is low and the resource optimization index (ROI) is also low, the system further determines that the resource waste or efficiency loss is caused by factors such as overstaffing, unreasonable work hour arrangement, and unbalanced equipment scheduling, and clarifies the optimization priorities and adjustment directions. Based on constraints such as task priority, schedule requirements, and resource availability, the system uses intelligent scheduling and load balancing mechanisms to dynamically adjust personnel work hours and equipment usage plans.

[0130] For personnel or work groups with excessively high working hours but low efficiency, the system can suggest reducing their time allocation or transferring some tasks to more efficient teams. For processes with low equipment utilization but urgent needs, the system can automatically adjust equipment scheduling plans, prioritizing equipment supply on the critical path. The system can also combine predictive algorithms to estimate the workload and resource requirements for a period of time in the future, optimizing scheduling and equipment configuration in advance to avoid temporary shortages or redundancies. The adjusted working hours and equipment usage plans will be automatically pushed to the construction scheduling system, personnel attendance module, or equipment management platform. At the same time, the system continuously collects execution feedback data (such as adjusted actual working hours, equipment operating status, etc.), forming a closed-loop control of "optimization-execution-feedback".

[0131] III. Real-time Financial Health Report Generation and Intelligent Alerts:

[0132] Based on multidimensional data on current financial coordination index, expenditure and resource status, a visualized and structured financial health report is automatically generated. Through an intelligent message push and early warning mechanism, potential financial risks and resource issues are promptly communicated to project management to support decision-making and reduce lag effects. The processing logic is as follows:

[0133] The system automatically aggregates core data, including the Financial Coordination Index (FRCI), Funds Utilization Stability Index (FSI), Resource Optimization Index (ROI), execution status of major expenditure categories, and key resource utilization KPIs, to generate a comprehensive real-time financial health report. This report typically includes modules such as a financial status overview, alerts to abnormal indicators, trend analysis charts, and a summary of optimization suggestions.

[0134] Based on the current value and fluctuation trend of the Financial Coordination Index (e.g., consistently below the threshold for multiple periods, rapid decline, etc.), the system automatically determines the current financial health risk level (e.g., alert level, attention level, warning level, or emergency level) and matches the corresponding reminder method and recipients. For example, for "warning level" risks, the system can send warning notifications to key roles such as project managers and financial officers via mobile app push notifications, system pop-ups, emails, or SMS. The system continuously tracks changes in financial and resource status, regularly updates the content and risk level of the financial health report (e.g., daily / weekly), and dynamically adjusts reminder strategies and optimization suggestions based on the latest data to ensure that management always has a clear understanding of the true status and evolution trends of project financial and resource management.

[0135] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0136] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An optimization method for a financial management system for construction workers, characterized in that: The optimization method includes the following steps: S1: During the construction process, relevant financial data and resource usage data of construction personnel are collected. After the data collection is completed, the expenditure change curve is automatically analyzed, the expenditure growth rate index is calculated, and the resource utilization trajectory is monitored in real time to extract performance indicators. If any indicator exceeds the predetermined budget range or the resource utilization threshold, it is automatically judged as a financial anomaly. S2: After identifying financial anomalies, a fund usage stability index is constructed based on the expenditure change curve. At the same time, a resource optimization index is constructed based on resource utilization. The fund usage stability index and the resource optimization index are jointly input into an intelligent optimization model trained on historical data to automatically calculate the financial coordination index. If the financial coordination index is lower than the preset standard, it is automatically determined that financial management and resource allocation have not reached the optimal state. S3: Based on the real-time calculated financial coordination index and its fluctuations over different time periods, it will autonomously select and execute a series of optimization strategies, including intelligent budget adjustment and expenditure control, dynamic optimization of working hours and resources, and generation of real-time financial health reports and intelligent reminders; The stability index of fund utilization and the resource optimization index are jointly input into an intelligent optimization model trained on historical data to automatically calculate the financial coordination index. If the financial coordination index is lower than the preset standard, it is automatically determined that financial management and resource allocation have not reached the optimal state, including the following steps: The input variables are the capital utilization stability index and the resource optimization index; The model training data comes from the financial and resource data of similar historical engineering projects, including past fund usage stability index and resource optimization index, as well as corresponding resource conflict event records, project delays, and overrun ratios; The model outputs a financial coordination index, with a value ranging from 0 to 1, which reflects the degree of coordination and matching between the current financial expenditure arrangements and the resource utilization status. The financial coordination index output by the model is compared with a pre-set index threshold: If the financial coordination index value is greater than or equal to the index threshold, it indicates that the current financial expenditure and resource utilization have reached a state of coordination and balance, and the optimization process will not be triggered for the time being, but monitoring will continue. If the financial coordination index value is less than the index threshold, it will be automatically determined that the current financial management and resource allocation have failed to achieve optimal coordination. After identifying financial anomalies, a fund utilization stability index is constructed based on the expenditure change curve, including the following steps: The collected expenditure time series data is smoothed. Based on the smoothed expenditure series, the expenditure change amplitude between adjacent periods is calculated, and the mean and standard deviation of the fluctuation amplitude within the time window are statistically analyzed to measure the intensity and dispersion of expenditure fluctuations. Further analysis is needed to determine whether the overall trend of the expenditure sequence conforms to the expected pattern. If the expenditure curve shows multiple abnormal peaks in a short period of time, it is judged to be unstable. Based on the above characteristics of volatility, standard deviation, and trend consistency, a weighted comprehensive scoring mechanism is adopted to output a fund utilization stability index ranging from 0 to 1. Constructing a resource optimization index based on resource utilization includes the following steps: Multiple performance indicators are automatically obtained from resource utilization data, including working hours utilization rate, equipment operation rate, task completion rate, resource balance and idle rate; A comprehensive analysis of multiple performance indicators is conducted, and a multi-factor weighted scoring method is used to output an index that reflects the overall optimization level of resource utilization—the resource optimization index, whose value ranges from 0 to 1.

2. The method for optimizing the financial management system for construction personnel according to claim 1, characterized in that: After data collection is completed, the system automatically analyzes the expenditure change curve and calculates the rate of change in expenditure growth, including the following steps: The original expenditure time series data is denoised and aggregated by cumulative or mean at fixed periods to generate a smooth expenditure trend series. For each period's expenditure amount, calculate the difference between it and the previous period's amount, and then divide it by the previous period's amount to obtain the expenditure growth rate, which is used to reflect the increase or decrease in expenditure in the current period relative to the previous period. Using sliding window or statistical distribution methods, statistical analysis is performed on the expenditure growth rate series to identify outliers that deviate from the normal fluctuation range; The cumulative expenditure amount or single expenditure amount for each period is compared with the budget limit for the corresponding period or category in the project budget table. If the actual expenditure exceeds a certain percentage of the budget value, a financial overspending warning is triggered.

3. The method for optimizing the financial management system for construction personnel according to claim 2, characterized in that: Real-time monitoring of resource utilization trajectory, extraction of performance indicators, and automatic identification of financial anomalies if any indicator exceeds the predetermined budget range or resource utilization threshold, including the following steps: Compare performance indicators with pre-set resource utilization thresholds; If the maximum / minimum fluctuation difference of working hours for a certain job exceeds the set threshold, it indicates that there is uneven distribution of working hours or idle time. If the equipment utilization rate is below a certain lower limit, it indicates that there is equipment redundancy or unreasonable scheduling. If the schedule deviation rate exceeds ±10%, it indicates that it affects the connection of subsequent processes and the overall project duration. If any financial expenditure indicator exceeds the budget range, or any resource utilization performance indicator exceeds the corresponding threshold, the project will be automatically identified as having financial anomalies.

4. The method for optimizing the financial management system for construction personnel according to claim 2, characterized in that: Based on the real-time calculated financial coordination index and its fluctuations over different time periods, a series of optimization strategies will be autonomously selected and implemented, including the following steps: By dynamically analyzing the actual occurrence of each expenditure category, the progress of budget execution, and the status of financial coordination, the budget allocation plan for various financial expenditures can be optimized. Based on real-time collected work performance data and financial coordination status, automatically optimize staffing hours and equipment usage plans; Based on multidimensional data on the current financial coordination index, expenditure and resource status, a visualized and structured financial health report is automatically generated. Through an intelligent message push and early warning mechanism, potential financial risks and resource issues are communicated to project management.

5. The method for optimizing the financial management system for construction personnel according to claim 4, characterized in that: By dynamically analyzing the actual occurrence of each expenditure category, budget execution progress, and financial coordination status, the system automatically optimizes the budget allocation scheme for various financial expenditures and controls the pace of expenditure. The processing logic is as follows: In real time, the cumulative amount and budget of each expenditure category are summarized, the budget execution rate and expenditure deviation rate are calculated, and their changing trends are analyzed in terms of time. If the current financial coordination index is low, the system further combines the expenditure change curve and stability index to identify the expenditure categories that cause financial incoordination.

6. The method for optimizing a financial management system for construction workers according to claim 1, characterized in that: The relevant financial data of the construction personnel includes personnel wages, material costs, and equipment depreciation expenditures. The trend of these data changes over time is tracked in real time to generate expenditure change curves.

7. The method for optimizing a financial management system for construction workers according to claim 6, characterized in that: The resource usage data of the construction personnel includes work hour allocation, equipment utilization rate, and work progress. It monitors fluctuations and changes in resource utilization and plots resource efficiency trajectories.

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