Four-in-one construction economical efficiency analysis method and system for power transmission line project

Abstract

The invention relates to the technical field of electric power engineering management, and discloses a power transmission line engineering four-in-one construction economical efficiency analysis method and system. According to the method, a cost data acquisition module is used for continuously acquiring original data streams reflecting ontology cost, road construction cost, compensation cost and environmental water conservation cost from a plurality of monitoring nodes on a construction site; the cost fluctuation stage division module is used for carrying out multi-scale fluctuation characteristic analysis on the data stream, and identifying and marking stable, ascending and descending stages of the cost data; the expense fluctuation behavior quantification module is used for calculating the fluctuation frequency and fluctuation amplitude of four expenses in each stage; the environment interference factor evaluation module is used for calculating an environment interference factor for each expense on the basis of a historical fluctuation rule and the current construction environment parameters; and the economy situation fusion module is used for performing multi-source information fusion on the fluctuation frequencies, the fluctuation amplitudes and the environmental interference factors of the four expenses at the current stage to generate economy situation indexes.

Description

Technical Field

[0001] This invention relates to the field of power engineering management technology, specifically to a four-in-one construction economic analysis method and system for transmission line projects. Background Technology

[0002] Current economic analysis of transmission line construction projects mainly relies on periodic report statistics and static indicator comparisons. Existing technologies for monitoring construction costs are largely based on fixed-period summaries, failing to capture the dynamic fluctuations of cost data in real time. Data collection dimensions are limited, and different types of costs cannot be precisely categorized and monitored. Fluctuation analysis methods fail to identify the phased characteristics of cost changes. Behavioral quantification indicators are lacking, and the frequency and amplitude of fluctuations are not effectively measured. Environmental impact factors are insufficiently considered, and the differentiated impacts of changes in construction conditions on various costs are not quantified. Existing methods need to address key technical issues such as dynamic data acquisition, multi-scale fluctuation analysis, and the quantification of environmental factors.

[0003] Traditional economic analysis systems suffer from significant shortcomings in real-time performance and accuracy. Monitoring nodes are sparsely distributed, resulting in incomplete collection of key cost data. Data analysis methods are simplistic, failing to adequately extract multi-scale fluctuation characteristics. Fixed stage division thresholds cannot adapt to the cost variations across different projects. Behavioral quantification models are linearized, failing to accurately reflect the non-linear fluctuation characteristics of costs. Historical data utilization is insufficient, and the statistical representativeness of fluctuation patterns is inadequate. Environmental parameter collection is incomplete, leading to limited accuracy in calculating interference factors. Information fusion algorithms are highly complex, resulting in low efficiency in real-time analysis. The generation of situation indicators is delayed, hindering timely decision-making. Summary of the Invention

[0004] The purpose of this invention is to provide a four-in-one construction economic analysis method and system for power transmission line projects to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides a four-in-one construction economic analysis method for power transmission line projects, the method comprising: The cost data acquisition module continuously collects raw data streams reflecting the main body cost, road construction cost, compensation cost, and environmental protection cost from multiple monitoring nodes at the construction site of the power transmission line project. The cost fluctuation phase segmentation module performs multi-scale fluctuation feature analysis on the collected raw data stream, identifies the stable phase, rising phase and falling phase in the cost data, and marks the start and end times of each phase. The cost fluctuation behavior quantification module calculates the frequency and amplitude of fluctuations in the main body cost, road construction cost, compensation cost, and environmental protection cost for each defined cost fluctuation stage. The environmental interference factor assessment module calculates environmental interference factors for main body costs, road construction costs, compensation costs, and environmental protection costs based on the distribution patterns of fluctuation frequency and amplitude during historical cost fluctuation periods, combined with current construction environment parameters. The economic situation fusion module integrates multi-source information on the frequency and amplitude of fluctuations in the cost of the main body, road construction, compensation, and environmental protection costs during the current cost fluctuation phase, as well as the corresponding environmental interference factors, to generate economic situation indicators.

[0006] Preferably, the cost fluctuation stage division module performs the following operations: The original data stream is subjected to sliding window mean filtering to obtain a smoothed cost data sequence; Calculate the first-order difference sequence of the smoothed cost data sequence and identify the positions of zero-crossing points in the difference sequence; The cost data sequence is divided into multiple monotonic intervals based on the zero-crossing point. Continuous monotonic intervals in the same direction are merged to form cost fluctuation phases. Each cost fluctuation phase is assigned a type label: the interval where the difference value is consistently positive is marked as the rising phase, the interval where the difference value is consistently negative is marked as the falling phase, and the interval where the difference value fluctuates slightly around zero is marked as the stable phase.

[0007] Preferably, the cost fluctuation behavior quantification module performs the following operations: For a defined cost fluctuation phase, extract the cost values ​​of all sampling points within that phase. The frequency of cost fluctuations in adjacent sampling points is obtained by counting the number of times the sign of the cost values ​​changes within the period and dividing the number of times by the duration of the period. Calculate the standard deviation of the cost values ​​for all sampling points within this stage, and use this standard deviation as the fluctuation range of the cost fluctuation stage.

[0008] Preferably, the environmental disturbance factor assessment module performs the following operations: Retrieve all historical cost fluctuation periods from the historical database that are similar to the current construction environment parameters; Calculate the mean frequency and mean amplitude of these historical cost fluctuation periods respectively; The ratio of the frequency of fluctuation in the current cost fluctuation phase to the historical average frequency of fluctuation is denoted as the frequency ratio. Calculate the ratio of the fluctuation range of the current cost fluctuation phase to the average historical fluctuation range, and denot it as the amplitude comparison; The weighted sum of frequency comparison and amplitude comparison is used as the environmental disturbance factor for the current cost fluctuation stage.

[0009] Preferably, the economic situation fusion module performs the following operations: The frequency, amplitude, and environmental interference factors of fluctuations in the main body cost, road construction cost, compensation cost, and environmental protection cost are received respectively. Multiply the fluctuation frequency and fluctuation amplitude of each cost by the environmental disturbance factor to obtain the stage disturbance factor of that cost. The comprehensive interference factor is obtained by summing the stage interference factors of the main body cost, the road construction cost, the compensation cost, and the environmental protection cost. The comprehensive interference factor is normalized and mapped to the range of zero to one hundred to generate an economic situation indicator.

[0010] Preferably, the system further includes: The urgency assessment module for regulatory needs determines the level of urgency of economic regulation based on the degree of deviation between economic indicators and preset thresholds, as well as the duration of such deviation. The dynamic regulation strategy generation module is used to generate dynamic regulation strategies that include regulation targets and regulation boundaries based on the urgency level of economic regulation and in combination with a preset regulation rule library. The control command execution module is used to convert dynamic control strategies into specific control commands and issue them to the corresponding execution mechanisms during the construction process.

[0011] Preferably, the urgency determination module for regulation needs performs the following operations: Real-time comparison of economic performance indicators with preset economic target ranges; If the economic performance indicators exceed the economic target range, the absolute value of the excess portion is calculated and recorded as the instantaneous deviation. The length of time that a statistical economic trend indicator remains outside the economic target range is recorded as the duration of deviation. Multiply the instantaneous deviation by the duration of the deviation to obtain the urgency assessment value; The urgency level of economic regulation is determined based on the numerical range of the urgency assessment value.

[0012] Preferably, the dynamic control strategy generation module performs the following operations: Based on the determined urgency level, the corresponding basic control strategy template is matched from the control rule base; Obtain feedback data on the actual control effect within a preset time period prior to the current moment; The control parameters in the basic control strategy template are revised based on feedback data of actual control effects. Based on the current proportion of stage interference factors for the four types of costs, the revised control parameters are decomposed into control dimensions of main body costs, road construction costs, compensation costs, and environmental protection costs, forming a dynamic control strategy that includes specific control targets and control boundaries for each dimension.

[0013] Preferably, the control command execution module performs the following operations: Analyze the control objectives and control boundaries of each dimension in the dynamic control strategy; The control targets are quantified into actionable instructions, which include adjusting the batch of construction material orders, modifying the construction machinery usage plan, changing the temporary land occupation compensation plan, and optimizing the implementation pace of soil and water conservation measures. The operation instructions are packaged with the corresponding control boundary conditions and sent through the industrial communication network to the terminal execution controller responsible for procurement, machinery scheduling, land acquisition coordination, and environmental protection.

[0014] Preferably, the present invention also includes a four-in-one construction economic analysis method for transmission line projects, applied to the aforementioned four-in-one construction economic analysis system for transmission line projects, comprising the following steps: The raw data stream reflecting the costs of the power transmission line project, road construction, compensation, and environmental protection is continuously collected from multiple monitoring nodes at the construction site. Multi-scale fluctuation feature analysis was performed on the collected raw data stream to identify the steady phase, rising phase and falling phase in the cost data, and the start and end times of each phase were marked. For each stage of cost fluctuation, calculate the frequency and magnitude of fluctuations in the main body cost, road construction cost, compensation cost, and environmental protection cost within that stage. Based on the distribution patterns of frequency and amplitude of historical cost fluctuations, and combined with current construction environment parameters, environmental interference factors are calculated for the main body cost, road construction cost, compensation cost, and environmental protection cost. The frequency and magnitude of the fluctuations of the four types of costs during the current cost fluctuation phase, as well as the corresponding environmental interference factors, are fused from multiple sources to generate an economic situation indicator. The urgency level of economic regulation is determined based on the degree of deviation between economic indicators and preset thresholds, as well as the duration of such deviation. Based on the urgency level of economic regulation and combined with a pre-set regulatory rule base, a dynamic regulatory strategy containing regulatory targets and regulatory boundaries is generated. The dynamic control strategy is transformed into specific control instructions and issued to the corresponding execution agencies during the construction process.

[0015] Compared with the prior art, the beneficial effects of the present invention are: By analyzing the multi-scale fluctuation characteristics of the original cost data stream, three phases—stable, rising, and falling—were identified. Wavelet transform and time-series segmentation algorithms were employed to capture the fluctuation characteristics at different time scales. Variance analysis, trend analysis, and extreme value localization were used to accurately divide each phase and mark its start and end times. For each fluctuation phase, the fluctuation frequency and amplitude of each cost item were calculated. Fluctuation frequency was calculated using zero-crossing rate analysis and peak count statistics, while fluctuation amplitude was calculated using range and standard deviation. Each cost item was calculated independently and normalized.

[0016] Environmental interference factors are calculated based on historical fluctuation patterns and current construction environment parameters. A historical fluctuation characteristic benchmark is established through kernel density estimation, and combined with meteorological, geological, and other environmental data, multiple regression analysis is used to quantify the degree of environmental impact. The interference factors are dynamically updated based on real-time data, distinguishing between primary and secondary influencing factors.

[0017] Economic situation indicators are generated by fusing multi-source information on fluctuation frequency, fluctuation amplitude, and environmental disturbance factors. Evidence theory and a weighted aggregation algorithm are used to balance the contribution of each indicator, and optimization calculations are performed considering correlation and redundancy between indicators. Indicator values ​​are normalized, and confidence level assessments are implemented to ensure the reliability of the results. A dynamic update mechanism reflects economic changes in real time.

[0018] This system enables precise situational awareness of construction economics, providing data support for project management. Multi-scale analysis reveals patterns in cost changes, environmental assessment incorporates external factors, and multi-source fusion enhances the comprehensiveness of the assessment. It helps improve the accuracy and real-time nature of project economic analysis, supporting refined management of the construction process. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the working principle of the four-in-one construction economic analysis system for power transmission line projects described in this invention. Figure 2 A flowchart illustrating the module operations for the cost fluctuation phase; Figure 3 A flowchart illustrating the operation of the cost fluctuation behavior quantification module; Figure 4 A comparison chart of the average expenditure of four types of costs at each construction stage; Figure 5 This is a comparison chart of the average costs of four categories at each construction stage of a power transmission line project. Detailed Implementation

[0020] 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.

[0021] Please see Figure 1 This invention provides a four-in-one construction economic analysis method and system for transmission line projects. The method includes: a cost data acquisition module, a cost fluctuation stage segmentation module, a cost fluctuation behavior quantification module, an environmental interference factor assessment module, and an economic situation fusion module. The cost data acquisition module continuously collects raw data streams reflecting the main body cost, road construction cost, compensation cost, and environmental protection cost from multiple monitoring nodes at the transmission line construction site. The cost fluctuation stage segmentation module performs multi-scale fluctuation characteristic analysis on the collected raw data streams, identifying stable, rising, and falling stages in the cost data, and marking the start and end times of each stage. The cost fluctuation behavior quantification module calculates the fluctuation frequency and amplitude of the main body cost, road construction cost, compensation cost, and environmental protection cost within each segmented cost fluctuation stage. The environmental interference factor assessment module calculates environmental interference factors for the main body cost, road construction cost, compensation cost, and environmental protection cost based on the distribution patterns of fluctuation frequency and amplitude in historical cost fluctuation stages, combined with current construction environment parameters. The economic situation fusion module integrates multi-source information on the frequency and amplitude of fluctuations in the cost of the main body, road construction, compensation, and environmental protection costs during the current cost fluctuation phase, as well as the corresponding environmental interference factors, to generate economic situation indicators.

[0022] Example 1: See Figure 2 In practical implementation, the cost fluctuation phase segmentation module performs sliding window mean filtering on the raw data stream collected from the monitoring nodes. The length of the sliding window is a configurable parameter. The mean filtering process yields a smoothed cost data sequence. The first-order difference sequence of the smoothed cost data sequence is then calculated. This first-order difference sequence is obtained by subtracting the value of the previous data point from the value of the next data point in the sequence. The formula for calculating the difference is expressed as: Where: characters in the formula Indicating in the index The first-order difference value at the character Indicates the index in the smoothed cost data sequence. Data point values, characters Indicates the index in the smoothed cost data sequence. The data point values. Identify the positions of zero-crossing points in the first-order difference sequence. A zero-crossing point is a point where the sign of the difference value changes, that is, a data point where the difference value changes from positive to negative or from negative to positive.

[0023] In some embodiments, the smoothed cost data sequence is divided into multiple monotonic intervals based on the identified zero-crossing points. Each monotonic interval consists of consecutive data points between two adjacent zero-crossing points. Consecutive monotonic intervals in the same direction are merged; these intervals are adjacent intervals with the same sign for their difference values. This merging operation forms the final cost fluctuation phase. Each merged cost fluctuation phase is assigned a type label according to the following rules: within a cost fluctuation phase, if the difference value remains positive, the phase is labeled as an upward phase; if the difference value remains negative, the phase is labeled as a downward phase; if the difference value fluctuates slightly around zero, the phase is labeled as a stable phase.

[0024] It is understandable that the size of the sliding window can be adjusted according to the data sampling frequency to adapt to different data fluctuation characteristics. It is also understandable that the threshold range for determining whether the difference value fluctuates slightly near zero is a configurable parameter used to distinguish between a stable phase and a phase with a clear trend.

[0025] Example 2: See Figure 3 In practical implementation, the cost fluctuation behavior quantification module performs operations on a cost fluctuation stage defined by the cost fluctuation stage segmentation module. It extracts the cost values ​​of all sampling points within this cost fluctuation stage. These cost values ​​are raw measurements obtained directly from the preprocessed data stream. The module counts the number of sign changes in the cost values ​​of adjacent sampling points within this cost fluctuation stage. A sign change refers to the change in the sign of the cost value difference between two adjacent sampling points from positive to negative or vice versa. The count is a cumulative value. Dividing the count of sign changes by the duration of this cost fluctuation stage yields the fluctuation frequency of this stage. The duration is the time difference between the start and end times of this cost fluctuation stage.

[0026] In some embodiments, the cost fluctuation behavior quantification module calculates the fluctuation amplitude of this cost fluctuation period. The fluctuation amplitude is calculated based on the standard deviation of the cost values ​​at all sampling points within this cost fluctuation period. The formula for calculating the standard deviation is expressed as: Where: characters in the formula This represents the calculated standard deviation result, represented by the character. Indicates the total number of sampling points included in the current cost fluctuation period, character Indicates the first period within the current cost fluctuation phase The cost value for each sampling point, character This represents the arithmetic mean of cost values ​​at all sampled points during the current cost fluctuation period. The calculated standard deviation... This is defined as the fluctuation range during the current period of cost fluctuation.

[0027] It is understandable that for different types of costs, including infrastructure costs, road construction costs, compensation costs, and environmental protection costs, the cost fluctuation behavior quantification module independently executes the above calculation process to obtain the fluctuation frequency and fluctuation amplitude of each type of cost within a specific cost fluctuation stage. It is understood that fluctuation frequency reflects how frequently cost data changes per unit time, while fluctuation amplitude reflects the dispersion of cost data around its average level. In some embodiments, the calculated fluctuation frequency and fluctuation amplitude are stored and associated with the corresponding cost fluctuation stage identifier for use by the environmental interference factor assessment module. After completing the calculation for one cost fluctuation stage, the cost fluctuation behavior quantification module automatically executes the same quantification process for the next cost fluctuation stage to be processed.

[0028] Example 3: In specific implementation, the environmental interference factor assessment module retrieves all historical cost fluctuation stages from the historical database that are similar to the current construction environment parameters. The current construction environment parameters include matching indicators across multiple dimensions such as geographical conditions, climate conditions, and construction progress stages. The mean fluctuation frequency and mean fluctuation amplitude of these matching historical cost fluctuation stages are calculated respectively. The mean fluctuation frequency is the arithmetic mean of the fluctuation frequencies of all corresponding cost fluctuation stages in the historical data, and the mean fluctuation amplitude is the arithmetic mean of the fluctuation amplitudes of all corresponding cost fluctuation stages in the historical data. The ratio of the fluctuation frequency of the current cost fluctuation stage to the historical mean fluctuation frequency is calculated, and this ratio is recorded as the frequency ratio. The ratio of the fluctuation amplitude of the current cost fluctuation stage to the historical mean fluctuation amplitude is calculated, and this ratio is recorded as the amplitude ratio. The frequency ratio and amplitude ratio are then weighted and summed. The formula for the weighted sum is expressed as: Where: characters in the formula This represents the calculated environmental interference factor, represented by the character. This represents a pre-defined weighting coefficient for frequency comparison; characters This represents the calculated frequency comparison, for each character. This represents a pre-defined weighting coefficient for the relative magnitudes, and the character... This indicates a relative comparison of the calculated magnitudes. The calculated weighted sum. This refers to the environmental disturbance factors identified during the current period of cost fluctuations.

[0029] In some embodiments, the economic situation fusion module receives output data from the cost fluctuation behavior quantification module and the environmental interference factor assessment module, respectively. The output data covers the costs of the building structure, road construction, compensation, and environmental protection, specifically including the fluctuation frequency, fluctuation amplitude, and corresponding environmental interference factor for each cost. The fluctuation frequency and amplitude of each cost are multiplied by the environmental interference factor; the result of this multiplication is defined as the stage interference factor for that cost. The stage interference factors for the building structure cost, road construction cost, compensation cost, and environmental protection cost are summed; the result of this summation is defined as the comprehensive interference factor. The comprehensive interference factor is then normalized using a linear transformation method to map its numerical range to a closed interval of zero to one hundred; the resulting value is defined as the economic situation index. The specific implementation of the linear transformation method involves normalization processing that utilizes the statistical extreme values ​​of the comprehensive interference factor in historical data to achieve range mapping. Based on long-term accumulated historical cost fluctuation data, the system pre-calculates the minimum and maximum values ​​of the comprehensive interference factor as scaling benchmarks. For the comprehensive interference factor generated in real time, its value is linearly interpolated with the historical minimum and maximum values, so that the historical minimum value corresponds to the mapped zero value, the historical maximum value corresponds to the mapped one hundred value, and the intermediate values ​​are linearly transformed proportionally. This smoothly maps the actual range of the comprehensive interference factor to a closed interval of zero to one hundred, generating an economic situation indicator.

[0030] It is understandable that, regarding the weighting coefficients and The value of is a fixed parameter pre-set based on historical data analysis during system initialization. It can be understood that, for the four cost types—body cost, road construction cost, compensation cost, and environmental protection cost—the environmental interference factor assessment module independently executes the calculation process for environmental interference factors, and the economic situation fusion module also independently calculates the stage interference factor for each cost type. In some embodiments, the range of comprehensive interference factor values ​​used for normalization processing is based on the minimum and maximum values ​​determined by long-term historical data statistics. After the economic situation fusion module completes the calculation, the generated economic situation indicators are transmitted to the subsequent regulatory demand urgency judgment module for further analysis.

[0031] Example 4: In specific implementation, the urgency judgment module for control demand compares the economic situation indicators generated by the economic situation fusion module with the preset economic target range in real time. The economic target range is a numerical range preset by the user to define the normal fluctuation boundary of the economic situation indicators. If the economic situation indicators exceed the economic target range, the absolute value of the difference between the economic situation indicator value and the boundary value of the economic target range is calculated, and this absolute value is recorded as the instantaneous deviation. The duration for which the economic situation indicators remain outside the economic target range is recorded, starting from the moment the economic situation indicators first exceed the economic target range until they return to the economic target range, and this duration is recorded as the continuous deviation time. The product of the instantaneous deviation and the continuous deviation time is calculated, and the ratio of this product to a baseline time constant is defined as the urgency assessment value. The formula for calculating the urgency assessment value is expressed as: Where: characters in the formula This represents the calculated urgency assessment value, in characters. This represents the current economic situation indicator value, in characters. Indicates the upper or lower boundary value of the economic target range, character. Indicates the duration of statistical deviation, character This represents the preset baseline time constant. Based on the calculated urgency assessment value. The numerical range in which the economic regulation falls determines the urgency level of the regulation. Refer to Table 1 for the correspondence between urgency levels and numerical ranges.

[0032] Table 1: Correspondence between Urgency Assessment Values ​​and Control Levels In some embodiments, the dynamic control strategy generation module generates a dynamic control strategy containing control targets and control boundaries based on the urgency level determined by the control demand urgency judgment module and a preset control rule library. The control rule library stores basic control strategy templates corresponding to different urgency levels, which include preliminary directions and parameter ranges for cost control. Actual control effect feedback data for a preset time period prior to the current moment is obtained, recording the actual changes in economic indicators after the implementation of historical control strategies. The control parameters in the basic control strategy templates are corrected based on the actual control effect feedback data, with fine-tuning performed according to the deviation between the feedback data and the expected results. Combining the current stage interference factor proportions of the four types of costs, the corrected control parameters are decomposed into control dimensions of body cost, road construction cost, compensation cost, and environmental protection cost, forming a dynamic control strategy containing specific control targets and control boundaries for each dimension.

[0033] It is understandable that the reference time constant The value of is related to the total project duration and is used to normalize the time dimension. It can be understood that the upper and lower boundary values ​​of the economic target range are independent and can be set separately to adapt to different scenarios where the economic situation indicators deviate upward or downward. In some embodiments, the proportion of stage interference factors is obtained by calculating the ratio of the stage interference factor of a single cost to the sum of the stage interference factors of the four costs. The dynamic control strategy output by the dynamic control strategy generation module is a structured data object, which explicitly includes specific control target values ​​and control boundary conditions for the main body cost, road construction cost, compensation cost, and environmental protection cost.

[0034] See Figure 4 This chart visually presents the average expenditure distribution characteristics of main body costs, road construction costs, compensation costs, and environmental protection costs at different construction stages. Specifically, in the preliminary preparation stage, the average expenditure on main body costs is significantly higher than other costs, followed by road construction costs, while compensation costs and environmental protection costs are relatively lower. In the foundation construction stage, main body costs remain the core expenditure item, while the average expenditure on road construction costs and compensation costs has been adjusted somewhat compared to the preliminary preparation stage. In the main installation stage, main body costs remain at a high level, while the average expenditure on compensation costs has increased significantly compared to the previous two stages. In the completion and acceptance stage, the average expenditure on all costs is in a low range, with only main body costs being relatively prominent. The cost expenditure distribution characteristics in this chart can serve as basic data support for dividing cost fluctuation stages and quantifying fluctuation behavior. The differences in costs at each stage can assist in the assessment of environmental interference factors and the integration analysis of economic trends, providing a visual reference for the cost structure for judging the urgency of subsequent regulation needs and generating dynamic regulation strategies.

[0035] Example 5: In specific implementation, the dynamic control strategy generation module matches the corresponding basic control strategy template from the preset control rule library based on the urgency level determined by the control demand urgency judgment module. The basic control strategy template is a structured data template that includes preliminary control direction and parameter range, pre-set for different urgency levels. It acquires actual control effect feedback data within a preset time period prior to the current moment. This actual control effect feedback data records the difference between the actual and expected changes in economic indicators after the historical control strategy was implemented. Based on the actual control effect feedback data, the control parameters in the basic control strategy template are corrected. This correction process is achieved through a feedback adjustment coefficient, the formula for which is expressed as: Where: characters in the formula This represents the calculated feedback adjustment coefficient, character. Indicates the number of control effect feedback data sets included within a preset time period, characters Indicates the first Actual changes in economic indicators in the group feedback data, characters Indicates the first The expected changes in economic indicators corresponding to the group feedback data. The original control parameters in the basic control strategy template are compared with the feedback adjustment coefficients. Multiplying these values ​​yields the corrected control parameter values. Combining the current proportions of stage interference factors for the four types of costs (the proportion of stage interference factors refers to the ratio of the stage interference factor of a single cost to the sum of the stage interference factors of the four costs), the corrected control parameter values ​​are decomposed according to the proportions of stage interference factors into the control dimensions of the main body cost, road construction cost, compensation cost, and environmental protection cost, forming a dynamic control strategy that includes specific control target values ​​and control boundary conditions for each dimension.

[0036] In some embodiments, the control instruction execution module receives and parses the dynamic control strategy output by the dynamic control strategy generation module. The dynamic control strategy is a structured data object that clearly contains control objectives and control boundaries for each cost dimension. The control objectives for each dimension in the dynamic control strategy are quantified into executable operation instructions. These operation instructions include adjusting construction material order batches, modifying construction machinery usage plans, changing temporary land occupation compensation schemes, and optimizing the implementation pace of soil and water conservation measures. The quantification process transforms numerical objectives into specific instruction descriptions with clearly defined operational objects. The operation instructions are packaged with corresponding control boundary conditions. These control boundary conditions limit the numerical range or constraints for the execution of the operation instructions. The packaged data units are then distributed via the industrial communication network to the terminal execution controller responsible for procurement, machinery scheduling, land acquisition coordination, and environmental protection.

[0037] It's understandable that the feedback adjustment coefficient... The calculation relies on multiple sets of historical control effect feedback data, and its value reflects the overall deviation trend of historical control effects. It can be understood that the calculation of the stage interference factor proportion is dynamic, based on the latest stage interference factor data output by the economic situation fusion module. In some embodiments, the data packets sent to the terminal execution controller are encapsulated using standardized industrial communication protocols to ensure accurate transmission and parsing of instructions. After receiving the data packet containing the operation instructions and control boundary conditions, the terminal execution controller will drive the corresponding actuator to complete the control action according to its built-in control logic.

[0038] See Figure 5The study presents the average cost distribution characteristics of the main construction costs, road construction costs, compensation costs, and environmental protection costs under three construction phases: stable, rising, and falling. Specifically, the average cost of the main construction in the rising phase (approximately 2.5 million yuan) is significantly higher than that in the stable phase (approximately 1.2 million yuan) and the falling phase (approximately 1.3 million yuan), reflecting the intensity of resource input for the main construction activities in this phase. Road construction costs also peak in the rising phase (approximately 1.65 million yuan), reflecting the concentrated development of supporting road projects in this phase. The fluctuation range of compensation costs and environmental protection costs is relatively mild in each phase. Among them, compensation costs reach approximately 1 million yuan in the rising phase, while the average level of environmental protection costs remains below 300,000 yuan in each phase, which is consistent with the phased differences in external costs and environmental protection inputs during the construction process.

[0039] 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.

Claims

1. A four-in-one construction economic analysis system for power transmission line projects, characterized in that, The method includes: The cost data acquisition module continuously collects raw data streams reflecting the main body cost, road construction cost, compensation cost, and environmental protection cost from multiple monitoring nodes at the construction site of the power transmission line project. The cost fluctuation phase segmentation module performs multi-scale fluctuation feature analysis on the collected raw data stream, identifies the stable phase, rising phase and falling phase in the cost data, and marks the start and end times of each phase. The cost fluctuation behavior quantification module calculates the frequency and amplitude of fluctuations in the main body cost, road construction cost, compensation cost, and environmental protection cost for each defined cost fluctuation stage. The environmental interference factor assessment module calculates environmental interference factors for main body costs, road construction costs, compensation costs, and environmental protection costs based on the distribution patterns of fluctuation frequency and amplitude during historical cost fluctuation periods, combined with current construction environment parameters. The economic situation fusion module integrates multi-source information on the frequency and amplitude of fluctuations in the cost of the main body, road construction, compensation, and environmental protection costs during the current cost fluctuation phase, as well as the corresponding environmental interference factors, to generate economic situation indicators.

2. The four-in-one construction economic analysis system for transmission line projects as described in claim 1, characterized in that, The cost fluctuation phase segmentation module performs the following operations: The original data stream is subjected to sliding window mean filtering to obtain a smoothed cost data sequence; Calculate the first-order difference sequence of the smoothed cost data sequence and identify the positions of zero-crossing points in the difference sequence; The cost data sequence is divided into multiple monotonic intervals based on the zero-crossing point. Continuous monotonic intervals in the same direction are merged to form cost fluctuation phases. Each cost fluctuation phase is assigned a type label: the interval where the difference value is consistently positive is marked as the rising phase, the interval where the difference value is consistently negative is marked as the falling phase, and the interval where the difference value fluctuates slightly around zero is marked as the stable phase.

3. The four-in-one construction economic analysis system for transmission line projects as described in claim 2, characterized in that, The cost fluctuation behavior quantification module performs the following operations: For a defined cost fluctuation phase, extract the cost values ​​of all sampling points within that phase. The frequency of cost fluctuations in adjacent sampling points is obtained by counting the number of times the sign of the cost values ​​changes within the period and dividing the number of times by the duration of the period. Calculate the standard deviation of the cost values ​​for all sampling points within this stage, and use this standard deviation as the fluctuation range of the cost fluctuation stage.

4. The four-in-one construction economic analysis system for transmission line projects as described in claim 3, characterized in that, The environmental disturbance factor assessment module performs the following operations: Retrieve all historical cost fluctuation periods from the historical database that are similar to the current construction environment parameters; Calculate the mean frequency and mean amplitude of these historical cost fluctuation periods respectively; The ratio of the frequency of fluctuation in the current cost fluctuation phase to the historical average frequency of fluctuation is denoted as the frequency ratio. Calculate the ratio of the fluctuation range of the current cost fluctuation phase to the average historical fluctuation range, and denot it as the amplitude comparison; The weighted sum of frequency comparison and amplitude comparison is used as the environmental disturbance factor for the current cost fluctuation stage.

5. The four-in-one construction economic analysis system for transmission line projects as described in claim 4, characterized in that, The economic situation fusion module performs the following operations: The frequency, amplitude, and environmental interference factors of fluctuations in the main body cost, road construction cost, compensation cost, and environmental protection cost are received respectively. Multiply the fluctuation frequency and fluctuation amplitude of each cost by the environmental disturbance factor to obtain the stage disturbance factor of that cost. The comprehensive interference factor is obtained by summing the stage interference factors of the main body cost, the road construction cost, the compensation cost, and the environmental protection cost. The comprehensive interference factor is normalized and mapped to the range of zero to one hundred to generate an economic situation indicator.

6. The four-in-one construction economic analysis system for transmission line projects as described in claim 1, characterized in that, The system also includes: The urgency assessment module for regulatory needs determines the level of urgency of economic regulation based on the degree of deviation between economic indicators and preset thresholds, as well as the duration of such deviation. The dynamic regulation strategy generation module is used to generate dynamic regulation strategies that include regulation targets and regulation boundaries based on the urgency level of economic regulation and in combination with a preset regulation rule library. The control command execution module is used to convert dynamic control strategies into specific control commands and issue them to the corresponding execution mechanisms during the construction process.

7. The four-in-one construction economic analysis system for transmission line projects as described in claim 6, characterized in that, The module for determining the urgency of regulatory needs performs the following operations: Real-time comparison of economic performance indicators with preset economic target ranges; If the economic performance indicators exceed the economic target range, the absolute value of the excess portion is calculated and recorded as the instantaneous deviation. The length of time that a statistical economic trend indicator remains outside the economic target range is recorded as the duration of deviation. Multiply the instantaneous deviation by the duration of the deviation to obtain the urgency assessment value; The urgency level of economic regulation is determined based on the numerical range of the urgency assessment value.

8. The four-in-one construction economic analysis system for transmission line projects as described in claim 7, characterized in that, The dynamic control strategy generation module performs the following operations: Based on the determined urgency level, the corresponding basic control strategy template is matched from the control rule base; Obtain feedback data on the actual control effect within a preset time period prior to the current moment; The control parameters in the basic control strategy template are revised based on feedback data of actual control effects. Based on the current proportion of stage interference factors for the four types of costs, the revised control parameters are decomposed into control dimensions of main body costs, road construction costs, compensation costs, and environmental protection costs, forming a dynamic control strategy that includes specific control targets and control boundaries for each dimension.

9. The four-in-one construction economic analysis system for transmission line projects as described in claim 8, characterized in that, The control command execution module performs the following operations: Analyze the control objectives and control boundaries of each dimension in the dynamic control strategy; The control targets are quantified into actionable instructions, which include adjusting the batch of construction material orders, modifying the construction machinery usage plan, changing the temporary land occupation compensation plan, and optimizing the implementation pace of soil and water conservation measures. The operation instructions are packaged with the corresponding control boundary conditions and sent through the industrial communication network to the terminal execution controller responsible for procurement, machinery scheduling, land acquisition coordination, and environmental protection.

10. A four-in-one construction economic analysis method for transmission line projects, applied to the four-in-one construction economic analysis system for transmission line projects as described in any one of claims 1 to 9, characterized in that, Includes the following steps: The raw data stream reflecting the costs of the power transmission line project, road construction, compensation, and environmental protection is continuously collected from multiple monitoring nodes at the construction site. Multi-scale fluctuation feature analysis was performed on the collected raw data stream to identify the steady phase, rising phase and falling phase in the cost data, and the start and end times of each phase were marked. For each stage of cost fluctuation, calculate the frequency and magnitude of fluctuations in the main body cost, road construction cost, compensation cost, and environmental protection cost within that stage. Based on the distribution patterns of frequency and amplitude of historical cost fluctuations, and combined with current construction environment parameters, environmental interference factors are calculated for the main body cost, road construction cost, compensation cost, and environmental protection cost. The frequency and magnitude of the fluctuations of the four types of costs during the current cost fluctuation phase, as well as the corresponding environmental interference factors, are fused from multiple sources to generate an economic situation indicator. The urgency level of economic regulation is determined based on the degree of deviation between economic indicators and preset thresholds, as well as the duration of such deviation. Based on the urgency level of economic regulation and combined with a pre-set regulatory rule base, a dynamic regulatory strategy containing regulatory targets and regulatory boundaries is generated. The dynamic control strategy is transformed into specific control instructions and issued to the corresponding execution agencies during the construction process.

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