An engineering cost composition method and system based on feature analysis

Abstract

This invention relates to the field of data processing technology, specifically a method and system for engineering cost estimation based on feature analysis. The method includes: performing spectral feature decomposition on pressure pulsation signals to obtain a characteristic frequency distribution map characterizing abnormal jet energy dissipation; simultaneously acquiring the grout mix ratio and rheological parameters of the grouting area, and applying damping effect correction to the characteristic frequency distribution map based on the grout rheological parameters to obtain a normalized jet energy consumption characteristic map, thus forming a normalized jet energy consumption characteristic map set; dividing the grouting area into multi-level cost estimation risk sub-segments, tracking and monitoring the grout activity shift data and energy dissipation intensity data at the grout-soil interface in each sub-segment; calculating and obtaining the quota applicability coefficient and cost estimation deviation risk index for each sub-segment, and outputting the corresponding risk level. This invention solves the problem in existing technologies of failing to identify hidden costs caused by geological formations.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and is a method and system for engineering cost estimation based on feature analysis. Background Technology

[0002] In high-pressure jet grouting (such as the MJS or RJP methods), the project cost is usually calculated based on the current quota system, multiplying macroscopic parameters such as soil type, design pile diameter, and cement content by the machine operating cost. However, when encountering high-salt and high-viscosity composite strata, the actual cost often deviates from the quota budget, and traditional pricing methods are difficult to explain and quantify the difference. The strata are not inert media that passively receive jet energy, but rather active systems with complex physicochemical feedback. In the high-salt environment of coastal and river engineering projects, the surface of soil particles carries a heterogeneous electric field. When ultra-high-pressure grout impacts these charged particles at supersonic speeds, it triggers particle-induced microscopic cavitation erosion, ultimately forming a micron-sized high-temperature and high-pressure retardation layer at the nozzle outlet. This retardation layer reverses the physicochemical activity of the grout, meaning that before the grout reaches the pile position, its hydration exothermic curve is prematurely triggered, causing a large amount of kinetic energy to be converted into disordered turbulence, heat energy, and mechanical vibration, rather than being used for effective cutting and consolidation of the soil. Traditional pricing treats equipment wear and tear as a fixed amortization under financial statistics, failing to identify hidden costs such as premature deterioration of slurry activity caused by geological formation, attenuation of effective impact energy, and nonlinear wear of nozzles. This leads to a disconnect between the price benchmark in the bidding stage and the cost in the construction stage. Summary of the Invention

[0003] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0004] The technical problem to be solved by this invention is that the existing technology cannot identify the hidden costs caused by the strata, and proposes a method and system for engineering cost pricing based on feature analysis.

[0005] To achieve the above objectives, the technical solution of the engineering cost pricing method based on feature analysis of the present invention includes the following steps: S1: Access the geological survey database of the construction area, obtain the current borehole strata feature dataset and the benchmark strata dataset for quota compilation, and trigger the assessment mode of the accuracy of engineering cost pricing based on the difference between the two. Once the evaluation mode is triggered, step S2 is executed; S2: Collect jet pressure pulsation signals and grout instantaneous temperature rise signals near the nozzle during grouting, and perform spectral feature decomposition on the pressure pulsation signals to obtain a characteristic frequency distribution map for characterizing abnormal jet energy dissipation; S3: Simultaneously acquire the slurry mix ratio and rheological parameters of the grouting area, and correct the characteristic frequency distribution spectrum based on the slurry rheological parameters to obtain the normalized jet energy consumption characteristic spectrum. Extract the normalized jet energy consumption characteristic spectrum of each monitoring period to form a normalized jet energy consumption characteristic spectrum set. S4: Identify jet energy distortion sections in the grouting area based on the normalized jet energy consumption characteristic map set, divide the grouting area into multi-level component risk sub-segments, and track and monitor the grout activity shift data and energy dissipation intensity data of the grout-soil interface in each sub-segment. S5: Import the slurry activity offset data and energy dissipation intensity data into the pricing deviation risk quantification model, calculate the quota applicability coefficient and pricing deviation risk index of each sub-section, and output the corresponding risk level.

[0006] Preferably, S1 includes the following specific steps: S11: Access the geological survey database of the construction area to obtain the stratigraphic feature dataset of the construction area. The stratigraphic feature dataset includes: the current borehole stratigraphic feature dataset A and the benchmark stratigraphic dataset B for quota compilation, as follows: ; in, This represents the cation exchange capacity of the soil at the current borehole depth. This represents the initial shear strength of the soil at the current borehole depth. This represents the clay content of the soil at the current borehole depth. , , These are the cation exchange capacity, initial shear strength, and clay content corresponding to the standard formation conditions on which the current quotas are based; S12: Extract the current borehole formation feature dataset A and the benchmark formation dataset B for quota compilation, and perform a trigger judgment for the pricing accuracy evaluation mode. The trigger judgment strategy is as follows: preset the cation exchange capacity deviation threshold. Initial shear strength deviation critical value and the critical value of clay content deviation ; Calculate the absolute deviation between the current borehole formation characteristic value and the quota benchmark value: , , When any deviation exceeds the corresponding preset threshold, it is determined that there is a potential pricing deviation risk in the current hole segment, triggering the pricing accuracy assessment mode and executing step S2.

[0007] Preferably, S2 includes the following specific steps: S21: Utilizing a high-frequency dynamic pressure sensor and a thermocouple temperature sensor installed upstream of the nozzle of the high-pressure jet grouting device, the jet pressure pulsation time history signal is collected. and slurry instantaneous temperature time history signal ; S22: For pressure pulsation signals Perform a Fast Fourier Transform to decompose it into spectral characteristic functions. ,in For frequency, This represents the energy density per unit frequency bandwidth. S23: Extracting spectral feature functions The spectral feature function is then imported into the jet energy dissipation characteristic frequency identification strategy to identify the characteristic frequency range and its corresponding energy amplitude used to characterize cavitation energy consumption and turbulence energy consumption. Among them, the characteristic frequency range of cavitation is: The characteristic frequency range of turbulent energy consumption is The corresponding energy amplitudes are respectively and ; S24: Real-time output of characteristic frequency distribution maps for each monitoring period, each map containing at least the following frequencies. Energy density The energy amplitude of the identified cavitation features Energy amplitude characteristic of turbulent energy consumption This constitutes a characteristic frequency distribution spectrum set.

[0008] Preferably, in S22, the spectral characteristic function of the pressure pulsation signal Obtained through the following strategies: ; Where T is the duration of the time-domain signal truncation; This is the jet pressure pulsation time history signal; For frequency; The power spectral density function represents the signal energy density per unit frequency bandwidth. The characteristic frequency band of normal jet energy dissipation determined by the on-site calibration test is as follows: ,in and These are the lower and upper frequency limits of the frequency band, respectively. Calculate the characteristic value of jet energy dissipation ,as follows: ; in, This represents the total energy within the characteristic frequency band; when Exceeding the preset threshold This indicates that the jet is in an abnormal energy dissipation state.

[0009] Preferably, in S23, the jet energy dissipation characteristic frequency identification strategy further includes: identifying cavitation characteristic frequencies. and turbulent energy consumption characteristic frequency ,in The value range is 20kHz to 200kHz. The value range is 100Hz to 5kHz; Calculate the energy density within the two characteristic frequency bands respectively. and : ; in, It is the power spectral density function; The half-width of the characteristic frequency band; and The intensity of cavitation energy consumption and turbulence energy consumption are respectively characterized.

[0010] Preferably, S3 includes: S31: Obtain the grout mix ratio and rheological parameters of the grouting area. The grout mix ratio and rheological parameters include: grout water-cement ratio. slurry plastic viscosity and slurry dynamic shear force ; S32: Extract the slurry ratio and rheological parameters, and establish a regression model for the slurry damping coefficient; S33: Calculate the frequency damping correction factor using the slurry damping coefficient regression model. and the characteristic value of jet energy dissipation. After normalization correction, the normalized jet energy consumption characteristic value is obtained. : ; Wherein, the frequency damping correction factor The calculation strategy is as follows: ; in, , , These are the water-cement ratio, plastic viscosity, and dynamic shear force under the standard mix ratio, where the standard mix ratio refers to the slurry mix ratio on which the current quota was compiled; This represents the residual term of the regression model; These are the regression coefficients; The regression coefficients are fitted using the least squares method. ; S34: Energy density of the original characteristic frequency band and Apply the same damping correction, and then normalize... , and A normalized jet energy consumption characteristic map set was constructed.

[0011] Preferably, S4 includes the following specific steps: S41: Identify jet energy distortion sections in the grouting area based on the normalized jet energy consumption characteristic map set. The identification strategy is to use the normalized jet energy consumption characteristic values... Compared with normal energy consumption benchmark value When comparing, When the time period is determined, the segment corresponding to that monitoring period is identified as the jet energy distortion segment; in The preset energy consumption deviation threshold ranges from 0.10 to 0.30. Based on the extent to which normalized energy consumption exceeds the benchmark value The distortion segment is divided into 5 pricing risk levels, the first... The energy consumption excess range corresponding to the class level is ,in ; S42: Divide the grouting area along the depth direction into sections of length [length missing]. The continuous sub-segments, each sub-segment corresponding to a monitoring period; Extract the pricing risk level of each sub-segment and count the length of consecutive occurrence of each level within that sub-segment. Import the sub-segment risk concentration calculation strategy to calculate the risk concentration coefficient of each sub-segment. The calculation strategy is as follows: ; in, For the first sub-segment The total length of the depth interval in which the risk level of the class group appears consecutively; This indicates the maximum consecutive length among the five levels within the sub-segment; S43: Number the sub-segments in depth order, and classify and color-code the pricing risk level of the sub-segments for visualization output. Level 0 is not colored or is marked in gray. S44: Track and monitor the slurry activity shift data and energy dissipation intensity data at the slurry-soil interface in each component risk sub-segment. The slurry activity shift data includes: slurry hydration-induced temperature rise rate. Attenuation rate of effective contact distance with slurry ; The energy dissipation intensity data includes: normalized cavitation characteristic band energy density after damping multiplication correction. and normalized turbulent energy consumption characteristic band energy density .

[0012] Preferably, S5 includes the following specific steps: S51: Extract slurry activity shift data and energy dissipation intensity data, the data including: normalized cavitation characteristic band energy density. Normalized turbulent energy consumption characteristic band energy density , slurry hydration induced temperature rise rate and the attenuation rate of effective action distance of slurry ; S52: Import the energy dissipation intensity data into the jet energy distortion deviation assessment strategy to calculate the energy dissipation deviation index. The jet energy distortion deviation assessment strategy is specifically as follows: ; in, , These are the energy density reference values ​​for the characteristic frequency bands of cavitation cavitation and turbulent energy consumption under normal jet conditions, respectively. The normalized cavitation characteristic band energy density; This is the reference value for the normalized cavitation characteristic band energy density under normal conditions. , Let be the weighting coefficient, satisfying ; S53: Import the slurry activity deviation data into the slurry activity deviation assessment strategy and calculate the activity deviation index. The specific strategy for evaluating the slurry activity deviation is as follows: ; in, , These are the baseline values ​​for the slurry hydration-induced temperature rise rate and the slurry effective action distance attenuation rate under normal jet conditions, respectively. , Let be the weighting coefficient, satisfying ; S54: Energy Consumption Deviation Index and activity deviation index In the import of the quota applicability coefficient calculation strategy, the quota applicability coefficient of each sub-section is calculated and obtained. Price deviation risk index The calculation strategy is as follows: ; ; in, , This is the bias sensitivity coefficient; S55: Extract the quota applicability coefficient and price deviation risk index for each pricing risk sub-segment, and output the corresponding risk level according to the preset risk threshold. when Output low risk when Outputting medium risk at the time, when It outputs high risk in real time; at the same time, it generates a price deviation warning report, prompting the cost engineer to conduct a special review of the section.

[0013] In S55, the price deviation early warning report includes: the location range of each sub-section, the quota applicability coefficient, the price deviation risk index, the risk level, the risk concentration coefficient, and the handling suggestions, which serve as the basis for decision-making in engineering cost review.

[0014] In addition, the engineering cost pricing system based on feature analysis of the present invention includes the following modules: The evaluation mode triggering module, the data acquisition module, the spectrum feature decomposition module, the pricing risk sub-segment division module, and the pricing deviation risk early warning module are all included. The assessment mode triggering module accesses the geological survey database of the construction area, obtains the current borehole stratum characteristic dataset and the quota compilation benchmark stratum dataset, and determines the triggering of the engineering cost pricing accuracy assessment mode based on the difference between the two. The acquisition module acquires the jet pressure pulsation signal and the instantaneous temperature rise signal of the grout near the nozzle during the grouting process, and performs spectral feature decomposition on the pressure pulsation signal to obtain a characteristic frequency distribution map for characterizing abnormal jet energy dissipation. The spectral feature decomposition module obtains the slurry ratio and rheological parameters of the grouting area, and performs damping effect correction on the characteristic frequency distribution map based on the slurry rheological parameters to obtain the normalized jet energy consumption feature map. It then extracts the normalized jet energy consumption feature map for each monitoring period to form a normalized jet energy consumption feature map set. The pricing risk sub-segment division module identifies jet energy distortion segments in the grouting area based on the normalized jet energy consumption characteristic map set, and divides the grouting area into multi-level pricing risk sub-segments, tracking and monitoring the grout activity shift data and energy dissipation intensity data of the grout-soil interface in each sub-segment. The pricing deviation risk early warning module imports slurry activity offset data and energy dissipation intensity data into the pricing deviation risk quantification model, calculates and obtains the quota applicability coefficient and pricing deviation risk index of each sub-section, and outputs the corresponding risk level.

[0015] Compared with the prior art, the technical effects of the present invention are as follows: 1. This invention transforms nonlinear losses such as jet energy distortion and premature hydration of slurry caused by formation into quantifiable pricing deviation risk index and quota applicability coefficient, thereby making up for the shortcomings of traditional quota pricing in that it cannot identify hidden costs such as invalid momentum amortization and slurry activity repair compensation, and significantly improving the accuracy and rationality of engineering cost prediction under special and complex formation conditions.

[0016] 2. This invention can effectively quantify the deviation in engineering pricing caused by the uncertainty of geological conditions, avoid the waste of resources caused by the construction party blindly increasing the amount of cement and pump pressure, and at the same time provide decision-making suggestions for the accrual of risk contingency funds and the identification of the optimal cost performance range, thus realizing refined risk-driven pricing. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating an engineering cost pricing method based on feature analysis according to the present invention. Figure 2 This is a schematic diagram of the structure of an engineering cost pricing system based on feature analysis according to the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0021] Example 1: like Figure 1 As shown, an embodiment of the present invention provides a method for engineering cost estimation based on feature analysis, such as... Figure 1 As shown, the specific steps include the following: S1: Access the geological survey database of the construction area, obtain the current borehole strata feature dataset and the benchmark strata dataset for quota compilation, and trigger the assessment mode of the accuracy of engineering cost pricing based on the difference between the two. Once the evaluation mode is triggered, step S2 is executed; S1 includes the following specific steps: S11: Access the geological survey database of the construction area to obtain the stratigraphic feature dataset of the construction area. The stratigraphic feature dataset includes: the current borehole stratigraphic feature dataset A and the benchmark stratigraphic dataset B for quota compilation, as follows: ; in, This represents the cation exchange capacity of the soil at the current borehole depth. This represents the initial shear strength of the soil at the current borehole depth. This represents the clay content of the soil at the current borehole depth, i.e., the percentage by mass of particles with a diameter less than 0.005 mm. , , These are the cation exchange capacity, initial shear strength, and clay content corresponding to the standard formation conditions on which the current quotas are based; S12: Extract the current borehole formation feature dataset A and the benchmark formation dataset B for quota compilation, and perform a trigger judgment for the pricing accuracy evaluation mode. The trigger judgment strategy is as follows: preset the cation exchange capacity deviation threshold. Initial shear strength deviation critical value and the critical value of clay content deviation ; Calculate the absolute deviation between the current borehole formation characteristic value and the quota benchmark value: , , When any deviation exceeds the corresponding preset threshold, it is determined that there is a potential pricing deviation risk in the current hole segment, triggering the pricing accuracy assessment mode and executing step S2.

[0022] In this embodiment, the formation characteristic parameter, namely cation exchange capacity... Initial shear strength Clay content All data were obtained through conventional geotechnical tests. Specifically, during the geological exploration phase before construction, soil samples were taken from the exploration boreholes and subjected to laboratory tests. The cation exchange capacity was determined using the barium chloride-sulfuric acid exchange method or the ammonium acetate method; the initial shear strength was determined using the vane shear test or the triaxial unconsolidated undrained shear test (UU); and the clay content was determined using the hydrometer method or the pipette method.

[0023] Because the geological parameters differ between different boreholes and depths within the same construction area, in this embodiment, firstly, the test results of each borehole at each depth are entered into a geological exploration database before construction, forming a geological feature dataset indexed by depth; then, during subsequent grouting construction, the corresponding data is retrieved from the database based on the current depth of the current borehole. , , value.

[0024] Standardized compilation of benchmark stratigraphic datasets In , , It is derived from the typical stratigraphic conditions on which the current quotas were compiled, and can be obtained by consulting the relevant quota compilation instructions or industry standards.

[0025] In another embodiment, a preset deviation threshold is used. , , Determined by those skilled in the art based on engineering experience: for example, collecting actual settlement data from multiple completed projects, calculating the statistical relationship between stratigraphic parameter deviations and final cost deviations, and selecting the critical value that maximizes the accuracy of cost deviation risk identification. It should be noted that the potential pricing deviation risk in this step refers to the fact that the current stratigraphic characteristics differ significantly from the quota benchmark, which may lead to a significant deviation between the cost calculated according to the quota and the actual construction cost. It is necessary to initiate subsequent steps to conduct a detailed evaluation of this section. This trigger condition is only used to screen key sections of concern and will not perform complex spectral analysis on all borehole sections, in order to save computing resources.

[0026] S2: Using the high-frequency dynamic pressure sensor and temperature sensor added to the high-pressure jet grouting device, the jet pressure pulsation signal and the instantaneous temperature rise signal of the grout near the nozzle are collected during the grouting process, and the pressure pulsation signal is decomposed into spectral features to obtain the characteristic frequency distribution map used to characterize the abnormal dissipation of jet energy. S2 includes the following specific steps: S21: Utilizing a high-frequency dynamic pressure sensor and a thermocouple temperature sensor installed upstream of the nozzle of the high-pressure jet grouting device, the jet pressure pulsation time history signal is collected. and slurry instantaneous temperature time history signal ; Among them, the sampling frequency of the high-frequency dynamic pressure sensor is not less than 500kHz, and the measurement range is 0~50MPa; the sampling frequency of the thermocouple temperature sensor is not less than 1Hz, and the measurement accuracy is ±0.5℃. S22: Ground data processing system for pressure pulsation signals Perform a Fast Fourier Transform to decompose it into spectral characteristic functions. ,in For frequency, This represents the energy density per unit frequency bandwidth. S23: Extracting spectral feature functions The spectral feature function is then imported into the jet energy dissipation characteristic frequency identification strategy to identify the characteristic frequency range and its corresponding energy amplitude used to characterize cavitation energy consumption and turbulence energy consumption. Among them, the characteristic frequency range of cavitation is: The characteristic frequency range of turbulent energy consumption is The corresponding energy amplitudes are respectively and ; S24: Real-time output of characteristic frequency distribution maps for each monitoring period, each map containing at least the following frequencies. Energy density The energy amplitude of the identified cavitation features Energy amplitude characteristic of turbulent energy consumption This constitutes a characteristic frequency distribution spectrum set.

[0027] For example, in this embodiment, a piezoelectric or strain gauge pressure sensor is selected as the high-frequency dynamic pressure sensor and installed in the high-pressure slurry delivery pipeline upstream of the nozzle. The distance from the nozzle should preferably be 0.5m to 1.5m to avoid excessive interference of the signal by local turbulence at the nozzle. It should also be noted that a sampling frequency of 500kHz or higher can meet the Nyquist sampling requirements for the cavitation characteristic frequency (20kHz to 200kHz). A K-type or T-type thermocouple is selected as the thermocouple and installed in the same position to monitor the slurry temperature change to help determine the hydration heat anomaly. In this embodiment, the Fast Fourier Transform (FFT) employs the conventional Cooley-Tukey algorithm. After windowing each segment of the time-domain signal (recommended segment length 0.1s–1.0s, corresponding to 5000–50000 sampling points), the power spectral density is calculated to obtain the spectral characteristic function. .

[0028] For example, the jet energy dissipation characteristic frequency identification strategy is as follows: Under normal construction conditions (i.e., standard strata and standard mix ratio), a pressure pulsation signal lasting 5 to 10 minutes is collected, and its average spectrum is obtained through FFT; observe whether there are frequency peaks in the spectrum that are significantly higher than the background noise. If a stable frequency peak appears in the range of 20kHz to 200kHz, its center frequency is determined as [the frequency of the jet energy dissipation characteristic frequency identification strategy]. If a stable frequency peak appears in the range of 100Hz to 5kHz, then its center frequency is determined as [value missing]. ; bandwidth half-width The peak half-width at half-maximum (FWHM) can be selected or set to a fixed value (e.g., 2kHz) based on engineering experience. If no obvious peak appears under normal operating conditions, characteristic signals can be excited by artificially creating mild cavitation (e.g., reducing inlet pressure) for calibration. and The identified characteristic frequency range and energy amplitude , .

[0029] It should be noted that the characteristic frequency distribution map set output in this step is stored in chronological order, with one map corresponding to each monitoring period. The time resolution is set according to the construction speed.

[0030] In S22, the spectral characteristic function of the pressure pulsation signal Obtained through the following strategies: ; Where T is the duration of the time-domain signal truncation; This is the jet pressure pulsation time history signal; For frequency; The power spectral density function represents the signal energy density per unit frequency bandwidth. The characteristic frequency band of normal jet energy dissipation determined by the on-site calibration test is as follows: ,in and These are the lower and upper frequency limits of the frequency band, respectively. Calculate the characteristic value of jet energy dissipation ,as follows: ; in, This represents the total energy within the characteristic frequency band; when Exceeding the preset threshold This indicates that the jet is in an abnormal energy dissipation state.

[0031] For example, the characteristic frequency band of normal jet energy dissipation Through on-site calibration tests, in another embodiment, the specific steps are as follows: First, select a grouting section (at least 2m in length) where the geological conditions meet the quota benchmark and the construction parameters are normal. Collect pressure pulsation signals for at least 30 consecutive time periods within this section, and calculate the power spectral density for each time period. Then, the average power spectral density is calculated. .

[0032] Then, observe Regarding the distribution on the frequency axis, it should be noted that jet energy dissipation is usually concentrated in the low frequency range (100Hz~5kHz, corresponding to turbulent energy dissipation) and the high frequency range (20kHz~200kHz, corresponding to cavitation).

[0033] Finally, the continuous frequency range in which the average power spectral density amplitude is significantly higher than the background noise was determined as the characteristic frequency band of normal jet energy dissipation. If the average power spectrum shows multiple separate frequency bands, then the dominant frequency band with the largest energy proportion is selected. For frequency band boundaries... , Take the frequency corresponding to when the amplitude drops to 1 / e or 1 / 2 of the peak amplitude.

[0034] In this embodiment, a preset threshold is used. This includes: based on engineering experience, a threshold of 1.5 times the normal average value can be used; It should be noted that if subsequent steps require separate treatment of cavitation and turbulent energy consumption, further steps are needed to address these issues. Divided into two sub-bands (e.g.) and ), and calculate respectively , .

[0035] In S23, the jet energy dissipation characteristic frequency identification strategy further includes: identifying cavitation characteristic frequencies. and turbulent energy consumption characteristic frequency ,in The value range is 20kHz to 200kHz. The value range is 100Hz to 5kHz; Calculate the energy density within the two characteristic frequency bands respectively. and : ; in, It is the power spectral density function; The half-width of the characteristic frequency band is determined through on-site calibration tests; and The intensity of cavitation energy consumption and turbulence energy consumption are respectively characterized.

[0036] In another embodiment, the cavitation characteristic frequency and turbulent energy consumption characteristic frequency The identification process is as follows: First, under normal construction conditions (standard strata, standard mix proportions), pressure pulsation signals are collected and the average power spectral density is calculated. .

[0037] Then, find the local peak with the highest amplitude in the range of 20kHz to 200kHz; the corresponding frequency is... Find the local peak with the highest amplitude in the range of 100Hz to 5kHz; the corresponding frequency is the [frequency value]. .

[0038] If there is no obvious peak within a certain range (the peak amplitude is less than 3 times the mean of the background noise), then this characteristic frequency may be missing, and the corresponding energy density... or It is considered as 0.

[0039] In this embodiment, the characteristic frequency band half-width Fixed values ​​are set based on engineering experience, such as cavitation cavitation. Turbulent energy consumption .

[0040] S3: Simultaneously acquire the slurry mix ratio and rheological parameters of the grouting area, and correct the characteristic frequency distribution spectrum based on the slurry rheological parameters to obtain the normalized jet energy consumption characteristic spectrum. Extract the normalized jet energy consumption characteristic spectrum of each monitoring period to form a normalized jet energy consumption characteristic spectrum set. S3 includes: S31: Obtain the grout mix ratio and rheological parameters of the grouting area. The grout mix ratio and rheological parameters include: grout water-cement ratio. slurry plastic viscosity and slurry dynamic shear force ; S32: Extract the slurry ratio and rheological parameters, and establish a regression model for the slurry damping coefficient; S33: Calculate the frequency damping correction factor using the slurry damping coefficient regression model. and the characteristic value of jet energy dissipation. After normalization correction, the normalized jet energy consumption characteristic value is obtained. : ; Wherein, the frequency damping correction factor The calculation strategy is as follows: ; in, , , These are the water-cement ratio, plastic viscosity, and dynamic shear force under the standard mix ratio, where the standard mix ratio refers to the slurry mix ratio on which the current quota was compiled; This represents the residual term of the regression model; These are the regression coefficients determined through field calibration experiments; The regression coefficients are fitted using the least squares method. ; In another embodiment, a slurry damping coefficient regression model is used to establish the relationship between slurry rheological parameters and frequency damping correction factors. The relationship between them is constructed as follows: At least five different grout ratios (covering common ranges of water-cement ratio 0.8–1.5, plastic viscosity 10–50 mPa·s, and dynamic shear stress 5–30 Pa) are selected, and jet grouting tests are conducted under standard formation conditions. For each grout, pressure pulsation signals are collected, and their... Simultaneously, the baseline energy consumption characteristic value was measured under the same construction parameters and no-grout air spraying conditions (here, only water spraying). The measured value of the frequency damping correction factor is... Perform multiple linear regression to obtain the regression coefficients. .

[0041] For the residual term of the regression model In this embodiment, the mean of the fitting residuals for each data point is taken. Furthermore, in the field calibration test, the standard proportions corresponding to... , , Choose one of the baseline proportions, such as This corresponds to ordinary silicate cement slurry.

[0042] S34: Energy density of the original characteristic frequency band and Apply the same damping correction, and then normalize... , and A normalized jet energy consumption characteristic map set was constructed.

[0043] S4: Identify jet energy distortion sections in the grouting area based on the normalized jet energy consumption characteristic map set, divide the grouting area into multi-level component risk sub-segments, and track and monitor the grout activity shift data and energy dissipation intensity data of the grout-soil interface in each sub-segment. S4 includes the following specific steps: S41: Identify jet energy distortion sections in the grouting area based on the normalized jet energy consumption characteristic map set. The identification strategy is to use the normalized jet energy consumption characteristic values... Compared with normal energy consumption benchmark value When comparing, When the time period is determined, the segment corresponding to that monitoring period is identified as the jet energy distortion segment; in The preset energy consumption deviation threshold ranges from 0.10 to 0.30. Based on the extent to which normalized energy consumption exceeds the benchmark value The distortion segment is divided into 5 pricing risk levels, the first... The energy consumption excess range corresponding to the class level is ,in , , There is no upper limit; the endpoint values ​​of each interval are determined through on-site calibration tests. for The sub-segment is defined as level 0 and does not participate in subsequent level statistics; It should be noted that, in this embodiment, the normal energy consumption baseline value The determination method is as follows: Select a grouting section with a length of not less than 2m, geological conditions that meet the quota benchmark, and normal construction parameters, continuously collect pressure pulsation signals for no less than 30 monitoring periods, and calculate the normalized jet energy consumption characteristic value for each period. Take the arithmetic mean of these values ​​as For energy consumption deviation threshold The value range is 0.10 to 0.30, and those skilled in the art can select it according to the engineering accuracy requirements; In another embodiment, an overshoot range boundary for the pricing risk level is provided. The division method is as follows: The range from 0 to the theoretical maximum value is divided into 5 intervals, using either an equal division method (e.g., each 20% is a level) or a percentile method based on historical data, even if each level contains a similar number of monitoring points.

[0044] S42: Divide the grouting area along the depth direction into sections of length [length missing]. The continuous sub-segments, each sub-segment corresponding to a monitoring time period. The value range is 0.1m to 1.0m; It should be noted that in high-pressure jet grouting, the entire grouting area is divided into several continuous sub-segments along the drilling depth direction, and the length of each sub-segment is denoted as . Considering, Too small a value would increase the computational load, while too large a value would mask local anomalies. Therefore, in this embodiment, a value of 0.2m to 0.5m is chosen to match the acceleration speed of high-pressure jet grouting (0.1m to 0.3m per minute) and the signal acquisition frequency.

[0045] Extract the pricing risk level of each sub-segment and count the length of consecutive occurrence of each level within that sub-segment. Import the sub-segment risk concentration calculation strategy to calculate the risk concentration coefficient of each sub-segment. The calculation strategy is as follows: ; in, For the first sub-segment The total length of the depth interval in which the risk levels of the class group price appear consecutively; it should be noted that consecutive appearance means that in the depth direction, adjacent monitoring points belong to the same risk level, without any other levels or level 0 inserted in between; This indicates the maximum consecutive length among the five levels within the sub-segment; It should be noted that for non-distortion segments (level 0), their length is not included. Calculate (i.e., the denominator only counts the total length of levels 1 to 5), and if there are no distorted segments within the sub-segment, then define... =0; It should also be noted that The value ranges from 0 to 1, and the larger the value, the more concentrated the high-risk area is within that sub-segment.

[0046] S43: Number the sub-segments in depth order, and classify and color-code the pricing risk level of the sub-segments for visualization output. Level 0 is not colored or is marked in gray. S44: Track and monitor the slurry activity shift data and energy dissipation intensity data at the slurry-soil interface in each component risk sub-segment. The slurry activity shift data includes the slurry hydration-induced temperature rise rate caused by jet energy distortion. Attenuation rate of effective contact distance with slurry , The value range is 0 to 1; The energy dissipation intensity data includes: normalized cavitation characteristic band energy density after damping multiplication correction. and normalized turbulent energy consumption characteristic band energy density .

[0047] In another embodiment, the slurry hydration-induced temperature rise rate The acquisition strategy is as follows: using a temperature sensor, during the monitoring period determined to be the jet energy distortion zone, the time history signal of the slurry temperature is acquired. The slope of the rising segment, i.e. .

[0048] In another embodiment, the attenuation rate of the effective contact distance of the slurry. The acquisition strategy is as follows: In the jet energy distortion section, compare the designed pile diameter with the actual pile diameter, and define... ; S5: Import the slurry activity offset data and energy dissipation intensity data into the pricing deviation risk quantification model, calculate the quota applicability coefficient and pricing deviation risk index of each sub-section, and output the corresponding risk level.

[0049] S5 includes the following specific steps: S51: Extract slurry activity shift data and energy dissipation intensity data, the data including: normalized cavitation characteristic band energy density. Normalized turbulent energy consumption characteristic band energy density , slurry hydration induced temperature rise rate and the attenuation rate of effective action distance of slurry ; S52: Import the energy dissipation intensity data into the jet energy distortion deviation assessment strategy to calculate the energy dissipation deviation index. The jet energy distortion deviation assessment strategy is specifically as follows: ; in, , The energy density benchmark values ​​for the characteristic frequency bands of cavitation and turbulence energy consumption under normal jet conditions were determined through field calibration tests. Specifically, the determination of the benchmark values ​​through field calibration tests included: continuously collecting data for no less than 30 monitoring periods under standard formation and standard mix conditions, and calculating the energy density of each period. , Then take the average value; The normalized cavitation characteristic band energy density; This is the reference value for the normalized cavitation characteristic band energy density under normal conditions. , Let be the weighting coefficient, satisfying ; S53: Import the slurry activity deviation data into the slurry activity deviation assessment strategy and calculate the activity deviation index. The specific strategy for evaluating the slurry activity deviation is as follows: ; in, , The reference values ​​for the slurry hydration-induced temperature rise rate and the slurry effective action distance attenuation rate under normal jet conditions were determined through field calibration tests. Specifically, in sections with standard strata, standard mix proportions, and qualified pile quality, no fewer than 30 monitoring periods were continuously collected, and calculations were performed. and Then take the average value, where The actual pile diameter is determined by core sampling or ultrasonic cross-hole detection and then calculated according to the definition. , Let be the weighting coefficient, satisfying Determined through the analytic hierarchy process or expert scoring method; In another embodiment, a grouting section with a length of not less than 2m, geological conditions meeting the quota benchmark, normal construction parameters, and qualified pile quality is selected. Pressure pulsation signals and temperature signals are collected for no less than 30 monitoring periods, and the values ​​for each period are calculated. , , Simultaneously, the actual pile diameter of the section is obtained through core sampling or ultrasonic testing, and the diameter of each time period is calculated. The average value of each indicator is taken as the corresponding benchmark value.

[0050] It should be noted that for the weighting coefficients in steps S52 and S53, the analytic hierarchy process (AHP) is used. First, at least five geotechnical engineering and cost engineering experts are invited to conduct pairwise comparisons and scoring of the relative importance of cavitation energy consumption, turbulence energy consumption, temperature rise rate, and attenuation rate over distance. A judgment matrix is ​​constructed, eigenvectors are calculated, and a consistency test (CR < 0.1) is performed. The arithmetic mean of the weights from each expert is taken as the final coefficient. In another embodiment, a typical value is as follows: In hard clay strata, the impact of cavitation is relatively small, and a value of [value missing] can be taken. In water-rich sand layers, the risk of cavitation is high, and it is advisable to take... For activity deviation, the rate of temperature rise directly reflects premature hydration and has a higher weighting, so it can be taken as... .

[0051] S54: Energy Consumption Deviation Index and activity deviation index In the import of the quota applicability coefficient calculation strategy, the quota applicability coefficient of each sub-section is calculated and obtained. Price deviation risk index The calculation strategy is as follows: ; ; in, , In this embodiment, the deviation sensitivity coefficient is used. , It was determined through regression analysis of historical engineering data and settlement price differences; it should be noted that... The closer the value is to 1, the more applicable the current quota is; A higher value indicates a higher risk of pricing deviation; S55: Extract the quota applicability coefficient and the pricing deviation risk index for each sub-section of the pricing risk, and output the corresponding risk level according to a preset risk threshold: When Output low risk, when Output medium risk, when Output high risk; at the same time, generate a pricing deviation warning report, prompting the cost engineer to conduct a special review of this section; Specifically: If the risk level is low risk, the disposal suggestion is routine record and no treatment is required; If the risk level is medium risk; When ra ≤ 0.4, the disposal suggestion is medium uniformity anomaly, and it is recommended to price at a 5% - 10% increase based on the quota unit price; When ra > 0.4, the disposal suggestion is local concentration anomaly, and it is recommended to conduct a special change quotation for the continuously high-risk level section.

[0052] If the risk level is high risk; When ra ≤ 0.4, the disposal suggestion is overall serious anomaly, and it is recommended to recompile the comprehensive unit price of this sub-section; When ra > 0.4, the disposal suggestion is extreme concentration anomaly, and it is recommended to stop work immediately, and conduct supplementary geological exploration and grouting process adjustment for the concentrated section.

[0053] It should be noted that the threshold value of 0.4 is a typical value set in this embodiment and can be adjusted according to engineering experience; it should also be noted that the content of the disposal suggestion is output as a part of the warning report and is only for decision-making reference in the pricing process.

[0054] It should also be noted that according to engineering practice experience, when the pricing deviation risk index RI ≤ 0.1, the deviation between the quota unit price and the actual cost is usually within ±5%, which can be regarded as low risk; when 0.1 < RI ≤ 0.3, the deviation can reach 5% - 15%, and attention is required; when RI > 0.3, the deviation may exceed 15%, and a special review should be conducted. This threshold can be adjusted according to the specific project's cost control requirements.

[0055] In S55, the pricing deviation warning report includes: the location range of each sub-section, the quota applicability coefficient, the pricing deviation risk index, the risk level, the risk concentration coefficient, and the disposal suggestion, as the decision-making basis for project cost audit.

[0056] Embodiment 2: As Figure 2 shown, a project cost pricing system based on feature analysis according to an embodiment of the present invention, as Figure 2 shown, includes the following modules: The evaluation mode triggering module, the data acquisition module, the spectrum feature decomposition module, the pricing risk sub-segment division module, and the pricing deviation risk early warning module are all included. The assessment mode triggering module accesses the geological survey database of the construction area, obtains the current borehole stratum characteristic dataset and the quota compilation benchmark stratum dataset, and determines the triggering of the engineering cost pricing accuracy assessment mode based on the difference between the two. The acquisition module acquires the jet pressure pulsation signal and the instantaneous temperature rise signal of the grout near the nozzle during the grouting process, and performs spectral feature decomposition on the pressure pulsation signal to obtain a characteristic frequency distribution map for characterizing abnormal jet energy dissipation. The spectral feature decomposition module obtains the slurry ratio and rheological parameters of the grouting area, and performs damping effect correction on the characteristic frequency distribution map based on the slurry rheological parameters to obtain the normalized jet energy consumption feature map. It then extracts the normalized jet energy consumption feature map for each monitoring period to form a normalized jet energy consumption feature map set. The pricing risk sub-segment division module identifies jet energy distortion segments in the grouting area based on the normalized jet energy consumption characteristic map set, and divides the grouting area into multi-level pricing risk sub-segments, tracking and monitoring the grout activity shift data and energy dissipation intensity data of the grout-soil interface in each sub-segment. The pricing deviation risk early warning module imports slurry activity offset data and energy dissipation intensity data into the pricing deviation risk quantification model, calculates and obtains the quota applicability coefficient and pricing deviation risk index of each sub-section, and outputs the corresponding risk level.

[0057] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0058] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0059] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0060] 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.

[0061] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A feature analysis-based method for engineering cost composition, characterized in that, The method includes: S1: Access the geological survey database of the construction area, obtain the current borehole strata feature dataset and the benchmark strata dataset for quota compilation, and trigger the assessment mode of the accuracy of engineering cost pricing based on the difference between the two. Once the evaluation mode is triggered, step S2 is executed; S2: Collect jet pressure pulsation signals and grout instantaneous temperature rise signals near the nozzle during grouting, and perform spectral feature decomposition on the pressure pulsation signals to obtain a characteristic frequency distribution map for characterizing abnormal jet energy dissipation; S3: Simultaneously acquire the slurry mix ratio and rheological parameters of the grouting area, and correct the characteristic frequency distribution spectrum based on the slurry rheological parameters to obtain the normalized jet energy consumption characteristic spectrum. Extract the normalized jet energy consumption characteristic spectrum of each monitoring period to form a normalized jet energy consumption characteristic spectrum set. S4: Identify jet energy distortion sections in the grouting area based on the normalized jet energy consumption characteristic map set, divide the grouting area into multi-level component risk sub-segments, and track and monitor the grout activity shift data and energy dissipation intensity data of the grout-soil interface in each sub-segment. S5: Import the slurry activity offset data and energy dissipation intensity data into the pricing deviation risk quantification model, calculate the quota applicability coefficient and pricing deviation risk index of each sub-section, and output the corresponding risk level.

2. The method according to claim 1, wherein, S1 includes the following specific steps: S11: Access the geological survey database of the construction area to obtain the stratigraphic feature dataset of the construction area. The stratigraphic feature dataset includes: the current borehole stratigraphic feature dataset A and the benchmark stratigraphic dataset B for quota compilation, as follows: ； wherein, is the cation exchange capacity of the soil at the current borehole depth; is the initial shear strength of the soil at the current borehole depth; is the clay content of the soil at the current borehole depth; , , These are the cation exchange capacity, initial shear strength, and clay content corresponding to the standard formation conditions on which the current quotas are based; S12: Extract the current borehole formation feature dataset A and the benchmark formation dataset B for quota compilation, and perform a trigger judgment for the pricing accuracy evaluation mode. The trigger judgment strategy is as follows: preset the cation exchange capacity deviation threshold. Initial shear strength deviation critical value and the critical value of clay content deviation ; Calculate the absolute deviation between the current borehole formation characteristic value and the quota benchmark value: , , When any deviation exceeds the corresponding preset threshold, it is determined that there is a potential pricing deviation risk in the current hole segment, triggering the pricing accuracy assessment mode and executing step S2.

3. The engineering cost pricing method based on feature analysis according to claim 2, characterized in that, S2 includes the following specific steps: S21: Utilizing a high-frequency dynamic pressure sensor and a thermocouple temperature sensor installed upstream of the nozzle of the high-pressure jet grouting device, the jet pressure pulsation time history signal is collected. and slurry instantaneous temperature time history signal ; S22: For pressure pulsation signals Perform a Fast Fourier Transform to decompose it into spectral characteristic functions. ,in For frequency, This represents the energy density per unit frequency bandwidth. S23: Extracting spectral feature functions The spectral feature function is then imported into the jet energy dissipation characteristic frequency identification strategy to identify the characteristic frequency range and its corresponding energy amplitude used to characterize cavitation energy consumption and turbulence energy consumption. Among them, the characteristic frequency range of cavitation is: The characteristic frequency range of turbulent energy consumption is The corresponding energy amplitudes are respectively and ; S24: Real-time output of characteristic frequency distribution maps for each monitoring period, each map containing at least the following frequencies. Energy density The energy amplitude of the identified cavitation features Energy amplitude characteristic of turbulent energy consumption This constitutes a characteristic frequency distribution spectrum set.

4. The engineering cost pricing method based on feature analysis according to claim 3, characterized in that, In S22, the spectral characteristic function of the pressure pulsation signal Obtained through the following strategies: ; Where T is the duration of the time-domain signal truncation; This is the jet pressure pulsation time history signal; For frequency; The power spectral density function represents the signal energy density per unit frequency bandwidth. The characteristic frequency band of normal jet energy dissipation determined by the on-site calibration test is as follows: ,in and These are the lower and upper frequency limits of the frequency band, respectively. Calculate the characteristic value of jet energy dissipation ,as follows: ; in, This represents the total energy within the characteristic frequency band; when Exceeding the preset threshold This indicates that the jet is in an abnormal energy dissipation state.

5. The engineering cost pricing method based on feature analysis according to claim 4, characterized in that, In S23, the jet energy dissipation characteristic frequency identification strategy further includes: identifying cavitation characteristic frequencies. and turbulent energy consumption characteristic frequency ,in The value range is 20kHz to 200kHz. The value range is 100Hz to 5kHz; Calculate the energy density within the two characteristic frequency bands respectively. and : ； in, It is the power spectral density function; The half-width of the characteristic frequency band; and The intensity of cavitation energy consumption and turbulence energy consumption are respectively characterized.

6. The engineering cost pricing method based on feature analysis according to claim 5, characterized in that, S3 include: S31: Obtain the grout mix ratio and rheological parameters of the grouting area. The grout mix ratio and rheological parameters include: grout water-cement ratio. slurry plastic viscosity and slurry dynamic shear force ; S32: Extract the slurry ratio and rheological parameters, and establish a regression model for the slurry damping coefficient; S33: Calculate the frequency damping correction factor using the slurry damping coefficient regression model. and the characteristic value of jet energy dissipation. After normalization correction, the normalized jet energy consumption characteristic value is obtained. : ; Wherein, the frequency damping correction factor The calculation strategy is as follows: ； in, , , These are the water-cement ratio, plastic viscosity, and dynamic shear force under the standard mix ratio, where the standard mix ratio refers to the slurry mix ratio on which the current quota was compiled; This represents the residual term of the regression model; These are the regression coefficients; The regression coefficients are fitted using the least squares method. ; S34: Energy density of the original characteristic frequency band and Apply the same damping correction, and then normalize... , and A normalized jet energy consumption characteristic map set was constructed.

7. The engineering cost pricing method based on feature analysis according to claim 6, characterized in that, S4 includes the following specific steps: S41: Identify jet energy distortion sections in the grouting area based on the normalized jet energy consumption characteristic map set. The identification strategy is to use the normalized jet energy consumption characteristic values... Compared with normal energy consumption benchmark value When comparing, When the time period is determined, the segment corresponding to that monitoring period is identified as the jet energy distortion segment; in The preset energy consumption deviation threshold ranges from 0.10 to 0.

30. Based on the extent to which normalized energy consumption exceeds the benchmark value The distortion segment is divided into 5 pricing risk levels, the first... The energy consumption excess range corresponding to the class level is ,in ; S42: Divide the grouting area along the depth direction into sections of length [length missing]. The continuous sub-segments, each sub-segment corresponding to a monitoring period; Extract the pricing risk level of each sub-segment and count the length of consecutive occurrence of each level within that sub-segment. Import the sub-segment risk concentration calculation strategy to calculate the risk concentration coefficient of each sub-segment. The calculation strategy is as follows: ; in, For the first sub-segment The total length of the depth interval in which the risk level of the class group appears consecutively; This indicates the maximum consecutive length among the five levels within the sub-segment; S43: Number the sub-segments in depth order, and classify and color-code the pricing risk level of the sub-segments for visualization output. Level 0 is not colored or is marked in gray. S44: Track and monitor the slurry activity shift data and energy dissipation intensity data at the slurry-soil interface in each component risk sub-segment. The slurry activity shift data includes: slurry hydration-induced temperature rise rate. Attenuation rate of effective contact distance with slurry ; The energy dissipation intensity data includes: normalized cavitation characteristic band energy density after damping multiplication correction. and normalized turbulent energy consumption characteristic band energy density .

8. The engineering cost pricing method based on feature analysis according to claim 7, characterized in that, S5 includes the following specific steps: S51: Extract slurry activity shift data and energy dissipation intensity data, the data including: normalized cavitation characteristic band energy density. Normalized turbulent energy consumption characteristic band energy density , slurry hydration induced temperature rise rate and the attenuation rate of effective action distance of slurry ; S52: Import the energy dissipation intensity data into the jet energy distortion deviation assessment strategy to calculate the energy dissipation deviation index. The jet energy distortion deviation assessment strategy is specifically as follows: ； in, , These are the energy density reference values ​​for the characteristic frequency bands of cavitation cavitation and turbulent energy consumption under normal jet conditions, respectively. The normalized cavitation characteristic band energy density; This is the reference value for the normalized cavitation characteristic band energy density under normal conditions. , Let be the weighting coefficient, satisfying ; S53: Import the slurry activity deviation data into the slurry activity deviation assessment strategy and calculate the activity deviation index. The specific strategy for evaluating the slurry activity deviation is as follows: ； in, , These are the baseline values ​​for the slurry hydration-induced temperature rise rate and the slurry effective action distance attenuation rate under normal jet conditions, respectively. , Let be the weighting coefficient, satisfying ; S54: Energy Consumption Deviation Index and activity deviation index In the import of the quota applicability coefficient calculation strategy, the quota applicability coefficient of each sub-section is calculated and obtained. Price deviation risk index The calculation strategy is as follows: ; ; in, , This is the bias sensitivity coefficient; S55: Extract the quota applicability coefficient and price deviation risk index for each pricing risk sub-segment, and output the corresponding risk level according to the preset risk threshold. when Output low risk when Outputting medium risk at the time, when It outputs high risk in real time; at the same time, it generates a price deviation warning report, prompting the cost engineer to conduct a special review of the section.

9. The engineering cost pricing method based on feature analysis according to claim 8, characterized in that, In S55, the price deviation early warning report includes: the location range of each sub-section, the quota applicability coefficient, the price deviation risk index, the risk level, the risk concentration coefficient, and the handling suggestions, which serve as the basis for decision-making in engineering cost review.

10. A feature-based engineering cost pricing system, used to implement the feature-based engineering cost pricing method as described in any one of claims 1-9, characterized in that, The system includes the following modules: The evaluation mode triggering module, the data acquisition module, the spectrum feature decomposition module, the pricing risk sub-segment division module, and the pricing deviation risk early warning module are all included. The assessment mode triggering module accesses the geological survey database of the construction area, obtains the current borehole stratum characteristic dataset and the quota compilation benchmark stratum dataset, and determines the triggering of the engineering cost pricing accuracy assessment mode based on the difference between the two. The acquisition module acquires the jet pressure pulsation signal and the instantaneous temperature rise signal of the grout near the nozzle during the grouting process, and performs spectral feature decomposition on the pressure pulsation signal to obtain a characteristic frequency distribution map for characterizing abnormal jet energy dissipation. The spectral feature decomposition module obtains the slurry ratio and rheological parameters of the grouting area, and performs damping effect correction on the characteristic frequency distribution map based on the slurry rheological parameters to obtain the normalized jet energy consumption feature map. It then extracts the normalized jet energy consumption feature map for each monitoring period to form a normalized jet energy consumption feature map set. The pricing risk sub-segment division module identifies jet energy distortion segments in the grouting area based on the normalized jet energy consumption characteristic map set, and divides the grouting area into multi-level pricing risk sub-segments, tracking and monitoring the grout activity shift data and energy dissipation intensity data of the grout-soil interface in each sub-segment. The pricing deviation risk early warning module imports slurry activity offset data and energy dissipation intensity data into the pricing deviation risk quantification model, calculates and obtains the quota applicability coefficient and pricing deviation risk index of each sub-section, and outputs the corresponding risk level.

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