A roadbed settlement prediction confidence analysis method and system

Abstract

The present invention discloses a roadbed settlement prediction confidence analysis method, including: S1, generating simulated settlement observation data, the simulated settlement observation data is generated based on discrete observation data with observation errors; S2, determining the applicability of a high-speed railway soft soil roadbed settlement prediction model under homogeneous natural foundation conditions and homogeneous sand well foundation conditions; S3, performing roadbed settlement prediction confidence analysis based on the simulated settlement observation data, the applicability of a high-speed railway soft soil roadbed settlement prediction model under homogeneous natural foundation conditions and homogeneous sand well foundation conditions; S1 includes: for the soft soil roadbed settlement problem under different foundation treatment forms and soil conditions, calculating and obtaining theoretical continuous data based on a homogeneous natural foundation calculation model and a sand well foundation calculation model; dividing the theoretical continuous data into observation period data and settlement late stage data, and discretizing to obtain discrete data; superimposing observation errors to generate discrete observation data with observation errors. A system, an electronic device and a storage medium are also disclosed.

Description

Technical Field

[0001] The present invention relates to the technical field of railway and roadbed settlement prediction and analysis, and in particular to a roadbed settlement prediction confidence analysis method and system. Background Art

[0002] Railway transportation occupies a backbone position in my country's overall transportation network. In recent years, based on the rapid development of my country's high-speed railways, railway construction and operation and maintenance have become an important driving force for my country's domestic economic growth and a beautiful business card for displaying its international image. my country has made plans for the medium- and long-term development of railways. However, since my country is a country with widespread soft soil, railway lines are bound to pass through a large number of soft soil areas. An important problem faced by the construction of high-speed railways in soft soil areas is the control of foundation settlement and post-construction settlement. Different settlement control standards are proposed for railways of different grades. The high smoothness requirements of high-speed railway ballastless tracks put forward more stringent requirements for post-construction settlement of the roadbed. Generally, the post-construction settlement is required to be no more than 15mm, which requires the final settlement of the roadbed to be accurately controlled during the construction process.

[0003] However, the prior art still has the following technical defects:

[0004] First, in current engineering design and construction, settlement calculation is mainly performed in the design stage and roadbed settlement observation, prediction and evaluation are performed in the construction stage to control post-construction settlement. However, due to the nonlinearity of soil stress-strain relationship, variability and anisotropy of geotechnical parameters, etc., the theoretical calculation method, especially the theoretical calculation method based on the layered summation method in the design stage, has large calculation deviations for the settlement development process and final settlement of the foundation. The roadbed settlement observation, prediction and evaluation in the construction stage is to extrapolate the settlement development process and final settlement based on the observation data. Since the settlement observation process is affected by many factors such as foundation treatment form, geological conditions, observation conditions, etc., and the influence of the above factors on the settlement prediction results is unclear, it is not easy to control the settlement of high-speed railway roadbed using settlement observation prediction.

[0005] Second, there is no mature technical solution in the prior art to study the influence of observation conditions on the mean and standard deviation of settlement prediction.

[0006] Third, settlement prediction is based on field measured data, using mathematical means such as curve fitting methods to extrapolate the final settlement of the roadbed and post-construction settlement. However, a large amount of test data is required for the analysis of settlement prediction confidence. The field conditions are complex, and it is difficult to effectively and accurately control the influence of foundation treatment form and soil conditions, observation accuracy, observation frequency, observation time, etc.; due to the variability and anisotropy of soil parameters, accurate test data cannot be obtained in the laboratory. Summary of the invention

[0007] The purpose of the present invention is to provide a roadbed settlement prediction confidence analysis method and system, which adopts observation conditions such as observation frequency, observation time, observation accuracy, etc., to generate simulated settlement observation data for the settlement theoretical calculation curve by discretization, interception and superposition of random errors, carry out curve fitting settlement prediction and analysis of samples, and study the influence of observation conditions on the settlement prediction mean and standard deviation.

[0008] A first aspect of the present invention is to provide a roadbed settlement prediction confidence analysis method, comprising:

[0009] S1, generating simulated settlement observation data, wherein the simulated settlement observation data is generated based on discrete observation data with observation errors;

[0010] S2, determine the applicability of the high-speed railway soft soil roadbed settlement prediction model under homogeneous natural foundation conditions and homogeneous sand well foundation conditions;

[0011] S3, performing a confidence analysis on the roadbed settlement prediction based on the simulated settlement observation data, the applicability of the high-speed railway soft soil roadbed settlement prediction model under the homogeneous natural foundation condition and the homogeneous sand well foundation condition.

[0012] Preferably, the S1 comprises:

[0013] S11, for the settlement problem of soft soil roadbed under different foundation treatment forms and different soil conditions, the corresponding theoretical continuous data under different conditions are calculated based on the homogeneous natural foundation calculation model and the sand well foundation calculation model;

[0014] S12, dividing the theoretical continuous data into observation period data and late settlement data according to different first settlement observation conditions, and discretizing the observation period data according to second settlement observation conditions to obtain discrete data;

[0015] S13, superimposing observation errors on discrete observation period data according to different third settlement observation conditions to generate discrete observation data with the observation errors, and generating the simulated settlement observation data based on the discrete observation data with the observation errors.

[0016] Preferably, the first settlement observation condition is observation duration; the second settlement observation condition is observation frequency; and the third settlement observation condition is observation accuracy.

[0017] Preferably, the observation accuracy refers to the influence of the observation accuracy conditions of Class I level, Class II level, Class III level, Class IV level and those with greater observation errors than Class IV level in engineering leveling measurement on the settlement prediction confidence, and the error in the deformation point elevation is used as the control indicator to distinguish the accuracy of leveling measurements at various levels; the observation frequency refers to the number or frequency of observations using the leveling observation method within the observation period; when analyzing the influence of observation frequency on the settlement prediction confidence, the different constant load periods after the completion of roadbed filling are divided into three observation periods: 0 to 3 months after the completion of filling, 4 to 6 months after the completion of filling, and 6 months after the completion of filling, and the influence of the observation frequency in the corresponding observation period on the prediction confidence compared with the current relevant regulations is determined respectively.

[0018] Preferably, S2 includes:

[0019] S21, using the theoretical model of settlement of homogeneous natural foundation and drainage shaft foundation, the theoretical settlement curves under different soil properties and foundation treatment conditions were obtained;

[0020] S22, processing the settlement theory curve based on commonly used observation conditions in engineering to generate simulated settlement observation data, and using a curve fitting model to predict the simulated settlement observation data;

[0021] S23, normalizing the soil properties and structural parameters of the homogeneous drainage shaft foundation based on the equivalent permeability coefficient method, comprehensively utilizing the correlation coefficient, relative error and coefficient of variation of the settlement prediction results to determine the applicability evaluation index of the settlement prediction model, and analyzing and obtaining the optimal use conditions of different curve fitting models based on the applicability evaluation index;

[0022] Preferably, S3 includes:

[0023] S31, based on the discrete observation data with the observation error, using a curve fitting method to perform prediction to obtain a prediction value corresponding to each group of observation data; repeating S31 to obtain multiple prediction values ​​to form a prediction result;

[0024] S32, performing statistical analysis on the prediction results to obtain a statistical mean value and a standard deviation of the predicted settlement;

[0025] S33, deriving corresponding influencing rules based on the predicted settlement statistical mean and the predicted settlement standard deviation respectively.

[0026] Preferably, the influencing rules include the influence of observation time on the statistical mean of predicted settlement, the influence of observation accuracy on the statistical mean of predicted settlement, the influence of observation frequency on the statistical mean of predicted settlement, the influence of soil conditions on the statistical mean of predicted settlement, the influence of observation time on the standard deviation of predicted settlement, the influence of observation accuracy on the standard deviation of predicted settlement, the influence of observation frequency on the standard deviation of predicted settlement and the influence of soil conditions on the standard deviation of predicted settlement.

[0027] The second aspect of the present invention further provides a roadbed settlement prediction confidence analysis system for implementing the method of the first aspect, comprising:

[0028] A data generation module (101) is used to generate simulated settlement observation data, wherein the simulated settlement observation data is generated based on discrete observation data with observation errors;

[0029] A prediction model applicability analysis module (102) is used to determine the applicability of a high-speed railway soft soil roadbed settlement prediction model under homogeneous natural foundation conditions;

[0030] A confidence analysis module (103) is used to perform confidence analysis on the roadbed settlement prediction based on the simulated settlement observation data and the applicability of the high-speed railway soft soil roadbed settlement prediction model under the homogeneous natural foundation condition.

[0031] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method described in the first aspect.

[0032] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor and execute the method described in the first aspect.

[0033] Beneficial effects of the method and system of the present invention:

[0034] (1) The predicted settlement statistical mean decreases with the increase of observation time. The two are exponentially related. The influence of observation time on the predicted settlement statistical mean is analyzed and their correlation is obtained. When the observation time is short, the systematic error of the predicted final settlement is large. The predicted settlement statistical mean decreases significantly with the increase of observation time, indicating that the systematic error of the predicted final settlement decreases significantly with the increase of observation time.

[0035] (2) The predicted settlement standard deviation decreases with the increase of observation time. The two are exponentially related. The influence of observation time on the predicted settlement standard deviation is analyzed and the correlation is obtained. When the observation time is short, it indicates that the accidental error of the predicted final settlement is large. The predicted settlement standard deviation decreases significantly with the increase of observation time, indicating that the accidental error of the predicted final settlement decreases significantly with the increase of observation time.

[0036] (3) The more frequent the observation frequency, the smaller the predicted settlement statistical mean value for the same observation time. The influence of observation frequency on the predicted settlement statistical mean value was analyzed and its correlation was obtained. The smaller the systematic error of the predicted final settlement, the smaller the influence of changing the observation frequency from 0 to 3 months, from 4 to 6 months, and after 6 months on the predicted settlement statistical mean value. When the observation time was 3 months and the observation frequency was 1 time / 5 days, 1 time / 1 week, and 1 time / 10 days, the relative errors of the predicted settlement statistical mean values ​​were 6.89%, 15.97%, and 10.99%, respectively.

[0037] (4) The influence of changing the observation frequency from 0 to 3 months, from 4 to 6 months, and after 6 months on the predicted settlement standard deviation is different. The influence of observation frequency on the predicted settlement standard deviation is analyzed and the correlation is obtained. Increasing the observation frequency from 0 to 3 months is not helpful for reducing the random error. When the observation period is 6 months, the predicted settlement standard deviation is 2.85 mm, 4.11 mm, and 4.45 mm when the observation frequency from 4 to 6 months is changed to 1 time / 10 days, 1 time / 2 weeks, and 1 time / 20 days, respectively.

[0038] (5) For repeated statistics of a large number of samples, the influence of observation accuracy on the statistical mean of predicted settlement is not significant. With the relaxation of observation accuracy, the standard deviation of predicted settlement increases exponentially. The influence of observation accuracy on the standard deviation of predicted settlement is analyzed and its correlation is obtained. The predicted settlement standard deviation obtained by using the observation prediction of Class I level is 50.7% of Class II level, 25.2% of Class III level, and 8.4% of Class IV level, which is 2.9% to 4.6% of the wider accuracy than Class IV level.

[0039] (6) The influence of observation conditions on the statistical mean and standard deviation of predicted settlement was obtained through analysis, and empirical formulas for the statistical mean and standard deviation of predicted settlement under different fitting curves and different soft soil foundation conditions as a function of observation accuracy, observation time, and observation period were established. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A first schematic diagram of a simulation observation data generation scheme provided according to an embodiment of the present invention;

[0042] Figure 2 A second schematic diagram of a simulated observation data generation solution provided according to an embodiment of the present invention;

[0043] Figure 3 A flow chart of a confidence analysis method for roadbed settlement prediction according to an embodiment of the present invention;

[0044] Figure 4 A time history diagram of the statistical mean of predicted settlement at different observation durations provided according to an embodiment of the present invention;

[0045] Figure 5 A histogram showing the influence of the observation frequency in the first three months on the predicted settlement statistical mean according to an embodiment of the present invention;

[0046] Figure 6 A bar graph showing the effect of observation frequency on the predicted settlement statistical mean after three months according to an embodiment of the present invention;

[0047] Figure 7 A bar graph showing the effect of observation frequency on the predicted settlement statistical mean after 6 months according to an embodiment of the present invention;

[0048] Figure 8 A histogram of the statistical mean values ​​of predicted settlement of a natural foundation with poor permeability at different observation accuracies provided according to an embodiment of the present invention;

[0049] Fig. 9 A histogram of the statistical mean values ​​of predicted settlement of a natural foundation with good permeability at different observation accuracies provided according to an embodiment of the present invention;

[0050] Fig.10 A histogram of the statistical mean values ​​of predicted settlement of natural foundations with poor permeability at different permeability coefficients provided according to an embodiment of the present invention;

[0051] Fig.11 A graph of the statistical mean reduction coefficients of predicted settlement of natural foundations with poor permeability at different permeability coefficients provided according to an embodiment of the present invention;

[0052] Fig.12A distribution diagram of prediction results under different observation time conditions for a natural foundation with poor permeability provided by an embodiment of the present invention;

[0053] Fig.13 A distribution diagram of prediction results under different observation time conditions for a natural foundation with good permeability provided by an embodiment of the present invention;

[0054] Fig.14 A time history diagram of predicted settlement standard deviations for different observation durations provided according to an embodiment of the present invention;

[0055] Fig.15 A time history diagram of standard deviation of predicted settlement of a natural foundation with poor permeability at different observation accuracies provided according to an embodiment of the present invention;

[0056] Fig.16 A time history diagram of standard deviation of predicted settlement of a natural foundation with good permeability at different observation accuracies provided according to an embodiment of the present invention;

[0057] Fig.17 A bar graph showing standard deviations of predicted settlements of natural foundations with poor permeability at different permeability coefficients provided according to an embodiment of the present invention;

[0058] Fig.18 A graph of standard deviation reduction factors for predicted settlement of natural foundations with poor permeability at different permeability coefficients provided according to an embodiment of the present invention;

[0059] Fig.19 A structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0062] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] Embodiment 1

[0064] like Figure 1-3 As shown, this embodiment provides a roadbed settlement prediction confidence analysis method, including:

[0065] S1, generating simulated settlement observation data, wherein the simulated settlement observation data is generated based on discrete observation data with observation errors; wherein the data is constant load period data;

[0066] As a preferred embodiment, the S1 includes:

[0067] like Figure 1 As shown, S11, for the settlement problem of soft soil roadbed under different foundation treatment forms and different soil conditions, the corresponding theoretical continuous data under different conditions are calculated based on the homogeneous natural foundation calculation model and the sand well foundation calculation model;

[0068] S12, dividing the theoretical continuous data into observation period data and post-settlement data according to different settlement observation conditions (in this embodiment, observation duration), and discretizing the observation period data according to certain settlement observation conditions (in this embodiment, observation frequency) to obtain discrete data;

[0069] like Figure 2 As shown, S13, according to different settlement observation conditions (observation accuracy in this embodiment), the observation error is superimposed on the discrete observation period data to generate discrete observation data with the observation error, and the simulated settlement observation data is generated based on the discrete observation data with the observation error.

[0070] As a preferred implementation, the settlement observation conditions include observation accuracy, observation frequency, and observation duration. Observation accuracy, observation frequency, and observation duration all have a significant impact on the deviation of settlement prediction results, but the quantitative impact of factors such as observation accuracy, observation frequency, and observation duration on the deviation of settlement prediction results is still unknown. The deviation of settlement prediction results is divided into systematic error and accidental error, and the levels of factors such as observation accuracy, observation duration, and observation frequency are limited to carry out research. The selection of each factor level is introduced as follows:

[0071] The observation accuracy mainly refers to the influence of the observation accuracy conditions of Class I, Class II, Class III, Class IV and Class IV observation errors in engineering leveling on the settlement prediction confidence. The mean error of the deformation point elevation is used as the control index to distinguish the accuracy of each level of leveling. The various measurement accuracy conditions are shown in Table 1. When analyzing the influence of observation accuracy on the settlement prediction confidence, the observation accuracy is analyzed by taking the mean error under the above-mentioned leveling accuracy conditions. When analyzing the influence of various curve fitting methods and other settlement observation conditions on the settlement prediction confidence, the observation accuracy is taken as Class III.

[0072] Table 1 Deformation measurement levels and accuracy requirements

[0073]

[0074] Observation frequency refers to the number or frequency of observations using the leveling observation method within the observation time. It is generally believed that multiple observations are required in a short period of time in the early stage after the completion of the roadbed filling, and the observation frequency needs to be reduced after a long time after the completion of the roadbed filling. The "Technical Regulations for Observation and Evaluation of Settlement and Deformation of Railway Engineering" has made relevant provisions on the observation frequency in different time periods, as shown in Table 2. When analyzing the influence of different curve fitting methods and other settlement observation conditions on the confidence of settlement prediction, the observation frequency is selected according to Table 2.

[0075] Table 2 Frequency of roadbed settlement observation

[0076]

[0077] The current relevant regulations do not discuss the impact of observation frequency in each observation period on the prediction confidence, and do not propose corresponding observation frequency recommendations for different engineering needs. When analyzing the impact of observation frequency on settlement prediction confidence, the different dead load periods after the completion of roadbed filling are divided into three observation periods: 0 to 3 months after the completion of filling, 4 to 6 months after the completion of filling, and 6 months after the completion of filling. The influence of the observation frequency in the corresponding observation period on the prediction confidence is determined respectively compared with the current relevant regulations. Table 3 is the calculation table of observation frequency applicable to the calculation, and the calculation mode of observation frequency is shown in Table 4.

[0078] Table 3 Observation frequency calculation table

[0079]

[0080] Table 4 Observation frequency calculation model table

[0081]

[0082] S2, determine the applicability of the high-speed railway soft soil roadbed settlement prediction model under homogeneous natural foundation conditions and homogeneous sand well foundation conditions;

[0083] As a preferred embodiment, S2 includes:

[0084] S21, using the theoretical model of settlement of homogeneous natural foundation and drainage shaft foundation, the theoretical settlement curves under different soil properties and foundation treatment conditions were obtained;

[0085] S22, processing the settlement theory curve based on commonly used observation conditions in engineering to generate simulated settlement observation data, and using a curve fitting model to predict the simulated settlement observation data;

[0086] S23, based on the equivalent permeability coefficient method, the soil properties and structural parameters of the homogeneous drainage shaft foundation are normalized, and the settlement prediction result correlation coefficient, relative error and coefficient of variation are comprehensively utilized to determine the applicability evaluation index of the settlement prediction model, and the optimal use conditions of different curve fitting models are analyzed based on the applicability evaluation index.

[0087] S3, performing a confidence analysis on the roadbed settlement prediction based on the simulated settlement observation data, the applicability of the high-speed railway soft soil roadbed settlement prediction model under the homogeneous natural foundation condition and the homogeneous sand well foundation condition;

[0088] like Figure 3 As shown, as a preferred implementation, S3 includes:

[0089] S31, based on the discrete observation data with the observation error, using four conventional curve fitting methods to perform prediction to obtain the prediction value corresponding to each group of observation data; repeating S31 to obtain multiple prediction values ​​to form a prediction result;

[0090] S32, performing statistical analysis on the prediction results to obtain a statistical mean value and a standard deviation of the predicted settlement;

[0091] In this embodiment, the predicted settlement statistical mean and the predicted settlement standard deviation are calculated according to formula (1) and formula (2) respectively.

[0092]

[0093] Where: N s ——The number of discrete observation data sets generated;

[0094] S k (k=1,2,...,N s ) — the predicted value obtained using each set of observed data;

[0095] μ——statistical mean of predicted settlement;

[0096] σ——standard deviation of predicted settlement.

[0097] S33, derive corresponding influence rules based on the predicted settlement statistical mean and predicted settlement standard deviation, respectively, the influence rules include the influence rule of observation time on the predicted settlement statistical mean, the influence rule of observation accuracy on the predicted settlement statistical mean, the influence rule of observation frequency on the predicted settlement statistical mean, the influence rule of soil conditions on the predicted settlement statistical mean, the influence rule of observation time on the predicted settlement standard deviation, the influence rule of observation accuracy on the predicted settlement standard deviation, the influence rule of observation frequency on the predicted settlement standard deviation and the influence rule of soil conditions on the predicted settlement standard deviation.

[0098] 1. The influence of observation conditions on the statistical mean of predicted settlement

[0099] The statistical mean of predicted settlement is the difference between the mean of the predicted value obtained by the curve fitting method through observation prediction and the theoretical value calculated by the roadbed settlement prediction method, divided by the theoretical value calculated by the settlement prediction method. It represents the systematic error of the prediction results caused by different curve fitting methods and different observation conditions under different soft soil foundation conditions. The factors and development laws that affect the generation of the system error are systematically explained, and the calculation method of the system error is established. Curve fitting uses curve fitting models such as the hyperbola method, exponential curve method, Asaoka method, and Hoshino method to predict the simulated settlement observation data.

[0100] 1. The influence of observation time on the statistical mean of predicted settlement

[0101] Figure 4 The figure is a time course curve of the statistical mean of predicted settlement at different observation times, taking the natural foundation with poor permeability as an example. The statistical mean of predicted settlement gradually approaches zero as the observation time gradually increases, indicating that as the observation time increases, the difference between the mean of the predicted value obtained by the curve fitting method through observation prediction and the theoretical value obtained by the roadbed settlement calculation method gradually decreases, and the settlement prediction result gradually approaches the true value. At the same time, as the observation time increases, the rate of change of the statistical mean of predicted settlement gradually decreases. When the observation time is short, the difference between the statistical mean of predicted settlement between two adjacent months is large. As the observation time increases, the difference between the statistical mean of predicted settlement between two adjacent months decreases significantly, indicating that as the observation time increases, the statistical mean of predicted settlement also gradually stabilizes.

[0102] Figure 4 The curve of the change of the predicted settlement statistical mean with the observation time can be described by formula (3):

[0103] δ=(μ-S ∞ ) / S ∞ =A1exp(-x1 / t1)+A2exp(-x1 / t2) (3)

[0104] Where: δ——relative error of settlement prediction

[0105] μ——statistical mean of predicted settlement (mm);

[0106] S0——final settlement (mm);

[0107] x1——Observation duration (month).

[0108] A1, t1, A2, t2——model fitting parameters.

[0109] 2. The influence of observation frequency on the statistical mean of predicted settlement

[0110] Figure 5 , Figure 6 and Figure 7 The bar graphs of the predicted settlement statistical means under mode ①, mode ②, and mode ③ are respectively. The changes of the predicted settlement statistical means with the observation time under different observation frequencies are consistent with the above description. As the observation time gradually increases, the predicted settlement statistical means gradually decreases. Whether it is mode ①, mode ②, or mode ③, increasing the observation frequency plays an important role in effectively controlling the predicted settlement statistical means. The difference is that mode ① can affect the predicted settlement statistical means during the entire observation time, the observation frequency of mode ② mainly affects the predicted settlement statistical means during the observation time after 3 months, and the observation frequency of mode ③ mainly affects the predicted settlement statistical means during the observation time after 6 months.

[0111] Mode ①, mode ②, and mode ③ have different degrees of influence on the statistical mean of predicted settlement. In mode ①, with the gradual relaxation of the observation frequency, the statistical mean of predicted settlement at the same observation time increases significantly. When the observation time is 3 months, the statistical mean of predicted settlement with an observation frequency of 1 time / 2 days in the first 3 months is 1.57%, the statistical mean of predicted settlement with an observation frequency of 1 time / 5 days in the first 3 months is 6.89%, the statistical mean of predicted settlement with an observation frequency of 1 time / 1 week in the first 3 months is 15.97%, the statistical mean of predicted settlement with an observation frequency of 1 time / 10 days in the first 3 months is 17.77%, and the statistical mean of predicted settlement with an observation frequency of 1 time / 1 month in the first 3 months is 26.88%. In mode ②, with the gradual relaxation of the observation frequency, the increase in the statistical mean of predicted settlement at the same observation time is also large, but it is smaller than the increase when the observation frequency is changed in the first three months. When the observation time is 6 months, the statistical mean of predicted settlement with an observation frequency of 1 time / 1 week after 3 months is 2.93%, the statistical mean of predicted settlement with an observation frequency of 1 time / 10 days after 3 months is 4.49%, the statistical mean of predicted settlement with an observation frequency of 1 time / 2 weeks after 3 months is 7.64%, the statistical mean of predicted settlement with an observation frequency of 1 time / 20 days after 3 months is 9.42%, and the statistical mean of predicted settlement with an observation frequency of 1 time / 1 month after 3 months is 10.49%. In mode ③, with the gradual relaxation of the observation frequency, the increase in the statistical mean of the predicted settlement at the same observation time is small. When the observation time is 12 months, the statistical mean of the predicted settlement with an observation frequency of 1 time / 2 weeks after 6 months is 1.73%, the statistical mean of the predicted settlement with an observation frequency of 1 time / 20 days after 6 months is 2.97%, the statistical mean of the predicted settlement with an observation frequency of 1 time / 1 month after 6 months is 4.06%, the statistical mean of the predicted settlement with an observation frequency of 1 time / 45 days after 6 months is 4.99%, and the statistical mean of the predicted settlement with an observation frequency of 1 time / 2 weeks after 6 months is 5.00%.

[0112] Using formula (3), the scatter plots of the change of the predicted settlement statistical mean with the observation time in mode ①, mode ②, and mode ③ can be fitted respectively, and the relationship between the model fitting parameters A1, t1, A2, and t2 of the fitting curve obtained by formula (3) and the observation frequency in mode ①, mode ②, and mode ③ are plotted respectively. The fitting curve can be determined by formula (4), formula (5), formula (6), and formula (7). The results of the coefficients of the fitting parameters of each model obtained by fitting are shown in Table 5.

[0113] A1=a1exp(-x2 / b1)+c1 (4)

[0114] t1=d1x2+e1 (5)

[0115] A2=a2exp(-x2 / b2)+c2 (6)

[0116] t2=d2x2+e2 (7)

[0117] In the formula: x2——observation frequency (days).

[0118] Table 5 Coefficients of model fitting parameters affected by observation frequency

[0119]

[0120] 3. The influence of observation accuracy on the statistical mean of predicted settlement

[0121] Figure 8 and Fig. 9 The bar graphs of the predicted settlement statistical mean under different observation accuracies for natural foundations with poor permeability and natural foundations with good permeability were drawn respectively. The change of the predicted settlement statistical mean with observation time under different observation accuracies is that as the observation time is gradually extended, the predicted settlement statistical mean gradually decreases. It can be seen from the figure that the observation accuracy has no effect on the predicted settlement statistical mean at the same observation time, indicating that the systematic error of settlement prediction is not affected by the leveling observation accuracy. The predicted settlement statistical mean at the same observation time under different observation accuracies is basically the same. The slight difference in the bar graph at the same observation time in the figure comes from the uncertainty of the observation data with observation errors during observation and prediction.

[0122] 4. The influence of soil conditions on the statistical mean of predicted settlement

[0123] Fig.10 The following is a bar chart of the statistical mean of predicted settlement under different permeability conditions, taking the natural foundation with poor permeability as an example. The change of the statistical mean of predicted settlement under different permeability conditions with the observation time is consistent with the above description. As the observation time gradually increases, the statistical mean of predicted settlement gradually decreases. However, it can also be seen that the ratio of the statistical mean of predicted settlement of the natural foundation with a permeability coefficient of 2.76E-4m / d to the statistical mean of predicted settlement of the natural foundation with a permeability coefficient of 4.92E-4m / d and 7.08E-4m / d is constant at different observation times. Therefore, a reduction factor can be used to correct the statistical mean of predicted settlement obtained under different permeability conditions.

[0124] Fig.11 is the correlation between the predicted settlement statistical mean reduction coefficient and the permeability coefficient, taking the natural foundation with poor permeability as an example. This relationship can be expressed by formula (8):

[0125] N=925.92593K v +0.74444 (8)

[0126] Where: N——reduction coefficient of the statistical mean of predicted settlement;

[0127] Kv ——Vertical permeability coefficient of natural foundation (m / d).

[0128] From the above analysis, the statistical mean calculation formulas for predicted settlement of natural foundation with poor permeability predicted by the exponential curve method are shown in equations (9), (10) and (11).

[0129] Mode ①:

[0130]

[0131] Mode ②:

[0132]

[0133] Mode ③:

[0134]

[0135] 2. The influence of observation conditions on the standard deviation of predicted settlement

[0136] The predicted settlement standard deviation is the discrete state of the predicted value obtained by the curve fitting method through observation and prediction. It characterizes the accidental error of the prediction result caused by different curve fitting methods and different observation conditions under different soft soil foundation conditions. This embodiment will systematically discuss the factors and development laws that affect the generation of this accidental error, and establish a calculation method for this accidental error. Curve fitting uses curve fitting models such as hyperbola method, exponential curve method, Asaoka method, and Hoshino method to predict the simulated settlement observation data.

[0137] 1. The influence of observation time on the standard deviation of predicted settlement

[0138] Fig.12 and Fig.13 The distribution diagrams of settlement prediction results of natural foundations with poor permeability and good permeability predicted by the exponential curve method and the star field method under different observation time conditions were drawn respectively. As the observation time increases, the discreteness of the settlement prediction results decreases, and the settlement prediction results are more concentrated near the statistical mean of the predicted settlement, indicating that as the observation time increases, the settlement prediction results become more accurate. The settlement prediction results of foundations under different soft soil foundation conditions predicted by different curve fitting methods have the same distribution law, but different values, indicating that the discreteness of the prediction results of foundations under different soft soil foundation conditions by different curve fitting methods is different.

[0139] Fig.14The figure is a time course curve of the standard deviation of predicted settlement at different observation times, taking a natural foundation with poor permeability as an example. The predicted settlement standard deviation gradually approaches zero as the observation time gradually increases, indicating that as the observation time increases, the discreteness of the predicted value obtained by the curve fitting method through observation prediction decreases, and the settlement prediction result gradually approaches the statistical mean of the predicted settlement. At the same time, as the observation time increases, the rate of change of the predicted settlement standard deviation gradually decreases. When the observation time is short, the difference in the predicted settlement standard deviation between two adjacent months is large. As the observation time increases, the difference in the predicted settlement standard deviation between two adjacent months decreases significantly, indicating that as the observation time increases, the predicted settlement standard deviation also gradually stabilizes. Fig.14 The curve of the change of the predicted settlement standard deviation with the observation time can be described by formula (12):

[0140] σ=A1exp(-x1 / t1)+A2exp(-x1 / t2) (12)

[0141] Where: σ——predicted settlement standard deviation (mm);

[0142] x1——observation duration (month);

[0143] A1, t1, A2, t2——model fitting parameters.

[0144] 2. The influence of observation frequency on the standard deviation of predicted settlement

[0145] For mode ①, when the observation time is within 1-3 months, the predicted settlement standard deviation of different observation frequencies in the first 3 months will have a large difference, and the more frequent the observation frequency, the larger the predicted settlement standard deviation at the same observation time. When the observation time is beyond 3 months, due to the same observation frequency, the predicted settlement standard deviations of different observation frequencies in the first 3 months are approximately parallel in the logarithmic coordinates, indicating that the influence of observation frequency on the predicted settlement standard deviation is the same. For mode ②, when the observation time is within 1-3 months, the predicted settlement standard deviations of different observation frequencies after 3 months are basically the same. When the observation time is 3-6 months, the predicted settlement standard deviations of different observation frequencies after 3 months are different, and the more frequent the observation frequency, the smaller the predicted settlement standard deviation at the same observation time. When the observation time is beyond 6 months, due to the same observation frequency, the predicted settlement standard deviations of different observation frequencies in the first 3 months are approximately parallel in the logarithmic coordinates, indicating that the influence of observation frequency on the predicted settlement standard deviation is the same. For mode ③, when the observation time is within 1-6 months, the predicted settlement standard deviations of different observation frequencies after 6 months are basically the same. When the observation time is 6-24 months, the predicted settlement standard deviations of different observation frequencies after 6 months are different, and the more frequent the observation frequency, the smaller the predicted settlement standard deviation at the same observation time. When the observation time is beyond 24 months, since the observation frequency used at this time is the same, the predicted settlement standard deviations of different observation frequencies in the first 3 months are approximately parallel in the logarithmic coordinates, indicating that the influence of the observation frequency on the predicted settlement standard deviation is the same.

[0146] When the observation time is 2 months, mode ① has a significant impact on the distribution of settlement prediction results. As the observation frequency gradually relaxes, the distribution of settlement prediction results is more concentrated near the statistical mean of predicted settlement. The settlement prediction distribution ranges of modes ② and ③ are roughly the same. Modes ② and ③ have basically no effect on the settlement prediction distribution range when the observation time is 2 months. When the observation time is 6 months, mode ① has a significant impact on the distribution of settlement prediction results. As the observation frequency gradually relaxes, the distribution of settlement prediction results is more concentrated near the statistical mean of predicted settlement. Mode ② also has a significant impact on the distribution of settlement prediction results. The influence of the observation frequency of mode ② on the distribution range of settlement prediction results is inconsistent with the influence of mode ① on the distribution range of settlement prediction results. As the observation frequency gradually increases, the distribution of settlement prediction results is more concentrated near the statistical mean of predicted settlement. The settlement prediction distribution ranges of mode ③ are roughly the same. Mode ③ has basically no effect on the settlement prediction distribution range when the observation time is 6 months. When the observation duration is 12 months, Mode ①, Mode ② and Mode ③ all have a significant impact on the distribution range of settlement prediction results. For Mode ①, as the observation frequency gradually relaxes, the distribution of settlement prediction results is more concentrated near the statistical mean of predicted settlement. For Mode ② and Mode ③, as the observation frequency gradually increases, the distribution of settlement prediction results is more concentrated near the statistical mean of predicted settlement.

[0147] In mode ①, mode ②, and mode ③, the relationship between the model fitting parameters A1, t1, A2, and t2 of the fitting curve obtained by formula (12) and the observation frequency changes, and the fitting curve can be determined by formula (13), formula (14), formula (15), and formula (16).

[0148] A1=a1exp(-x2 / b1)+c1 (13)

[0149] t1=d1x2+e1 (14)

[0150] A2=a2exp(-x2 / b2)+c2 (15)

[0151] t2=d2x2+e2 (16)

[0152] In the formula: x2——observation frequency (days).

[0153] 3. The influence of observation accuracy on the standard deviation of predicted settlement

[0154] Fig.15 and Fig.16The standard deviation time history diagrams of predicted settlement under different observation accuracy conditions for natural foundations with poor permeability and good permeability predicted by the exponential curve method and the star field method were drawn respectively. It can be seen from the two figures that under different observation accuracy conditions, the standard deviation of predicted settlement gradually decreases with the extension of observation time, indicating that with the extension of observation time, the discreteness of settlement prediction results gradually decreases, and the prediction results gradually concentrate near the statistical mean of predicted settlement. Under logarithmic coordinates, the curves of the change of predicted settlement standard deviation with observation time under different observation accuracy conditions are approximately parallel, that is, the ratio of predicted settlement standard deviation under different observation accuracy conditions does not increase with the extension of observation time. When the observation time is 3 months, the standard deviation of predicted settlement with observation accuracy of 2mm is 0.70 of that with observation accuracy of 4mm, the standard deviation of predicted settlement with observation accuracy of 1mm is 0.259 of that with observation accuracy of 4mm, and the standard deviation of predicted settlement with observation accuracy of 0.5mm is 0.128 of that with observation accuracy of 4mm. When the observation time is 12 months, the standard deviation of predicted settlement with observation accuracy of 2mm is 0.70 of that with observation accuracy of 4mm, the standard deviation of predicted settlement with observation accuracy of 1mm is 0.259 of that with observation accuracy of 4mm, and the standard deviation of predicted settlement with observation accuracy of 0.5mm is 0.128 of that with observation accuracy of 4mm. This shows that the influence of observation accuracy on the discreteness of settlement prediction results is consistent under different observation time. Due to the difference in the curve fitting method used under different soil conditions, the standard deviation of predicted settlement obtained under the same observation time and observation accuracy is different, and the influence of different observation accuracy on the discreteness of settlement prediction results is also different. For the natural foundation with poor permeability predicted by the exponential curve method, when the observation accuracy is 3 months, the standard deviations of predicted settlement obtained by observation accuracy of 0.5mm, 1mm, and 2mm are 4.26, 8.60, and 23.35 respectively, and the ratio of the standard deviation of predicted settlement with observation accuracy of 0.5mm, 1mm, and 2mm to that of observation accuracy of 4mm is as mentioned above. When the observation accuracy is 12 months, the standard deviations of predicted settlement obtained by observation accuracy of 0.5mm, 1mm, and 2mm are 1.08, 2.19, and 5.94 respectively, and the ratio of the standard deviation of predicted settlement with observation accuracy of 0.5mm, 1mm, and 2mm to that of observation accuracy of 4mm is as mentioned above.For the natural foundation with good permeability predicted by the Xingye method, when the observation accuracy is 3 months, the standard deviations of the predicted settlements obtained with observation accuracy of 0.5mm, 1mm, and 2mm are 0.81, 1.97, and 5.16 respectively, the standard deviation of the predicted settlements with an observation accuracy of 2mm is 0.187 of the observation accuracy of 4mm, the standard deviation of the predicted settlements with an observation accuracy of 1mm is 0.071 of the observation accuracy of 4mm, and the standard deviation of the predicted settlements with an observation accuracy of 0.5mm is 0.029 of the observation accuracy of 4mm. When the observation accuracy is 12 months, the standard deviations of the predicted settlements obtained with observation accuracy of 0.5mm, 1mm, and 2mm are 0.21, 0.50, and 1.31 respectively, the standard deviation of the predicted settlements with an observation accuracy of 2mm is 0.187 of the observation accuracy of 4mm, the standard deviation of the predicted settlements with an observation accuracy of 1mm is 0.071 of the observation accuracy of 4mm, and the standard deviation of the predicted settlements with an observation accuracy of 0.5mm is 0.029 of the observation accuracy of 4mm.

[0155] Distribution diagram of prediction results of natural foundation with poor permeability predicted by exponential curve method under different observation accuracy conditions. Comparing the three figures, it can be seen that when the observation time is the same, the more accurate the observation accuracy is, the smaller the discreteness of the settlement prediction results is, and the settlement prediction results are more concentrated near the statistical mean of the predicted settlement; the rougher the observation accuracy is, the greater the discreteness of the settlement prediction results is, and the settlement prediction results are distributed in a wider distribution range on both sides of the predicted settlement statistical mean. When the permeability coefficient is different, the discreteness of the settlement prediction with the same observation accuracy is basically the same.

[0156] The coefficients of the model fitting parameters in formula (13), formula (14), formula (15) and formula (16) are affected by the observation accuracy. Table 6 lists the values ​​of the coefficients of the model fitting parameters affected by the observation accuracy.

[0157] Table 6 Coefficients of model fitting parameters affected by observation accuracy

[0158]

[0159] In the formula: x3——observation accuracy (mm).

[0160] 4. The influence of soil conditions on the standard deviation of predicted settlement

[0161] Fig.17 The following is a bar chart of the standard deviation of predicted settlement under different permeability conditions, taking the natural foundation with poor permeability as an example. The change of the standard deviation of predicted settlement under different permeability conditions with the observation time is consistent with the above description. As the observation time gradually increases, the standard deviation of predicted settlement gradually decreases. However, it can also be seen that the permeability coefficient is 2.76E under different observation times. -4 The predicted settlement standard deviation and permeability coefficient of natural foundation is 4.92E -4 m / d and 7.08E-4 The ratio of the predicted settlement standard deviation of m / d natural foundation is constant, so a reduction coefficient can be used to correct the predicted settlement standard deviation obtained under different permeability conditions.

[0162] Fig.18 The correlation between the predicted settlement standard deviation reduction factor and the permeability coefficient is obtained by taking the natural foundation with poor permeability as an example. This relationship can be expressed by formula (17):

[0163] M=435.516K v +67.18 (17)

[0164] Where: M——reduction factor of predicted settlement standard deviation;

[0165] K v ——Vertical permeability coefficient of natural foundation (m / d).

[0166] From the above analysis, the calculation formulas for the standard deviation of predicted settlement of natural foundation with poor permeability predicted by the exponential curve method are as shown in equations (18), (19) and (20).

[0167] Mode ①:

[0168]

[0169] Mode ②:

[0170]

[0171] Mode ③:

[0172]

[0173] 3. The influence of different soft soil foundation conditions on the statistical mean and standard deviation of predicted settlement

[0174] Using the above method, the statistical mean and standard deviation of the predicted settlement predicted by different curve fitting methods under different foundation treatment forms and soil conditions will be calculated, and the calculation formulas for the statistical mean and standard deviation of the predicted settlement under the influence of observation conditions such as observation time x1, observation frequency x2, and observation accuracy x3 will be obtained.

[0175] 1. Influence of soil conditions on the statistical mean of predicted settlement under natural foundation conditions

[0176] For natural foundations with good permeability predicted by the Hoshino method:

[0177] Mode ①:

[0178]

[0179] Mode ②:

[0180]

[0181] Mode ③:

[0182]

[0183] 2. Influence of soil conditions on predicted settlement standard deviation under natural foundation conditions

[0184] For natural foundations with good permeability predicted by the Hoshino method:

[0185] Mode ①:

[0186]

[0187] Mode ②:

[0188]

[0189] Mode ③:

[0190]

[0191] Embodiment 2

[0192] This embodiment also provides a roadbed settlement prediction confidence analysis system, including:

[0193] A data generation module 101 is used to generate simulated settlement observation data, wherein the simulated settlement observation data is generated based on discrete observation data with observation errors;

[0194] The prediction model applicability analysis module 102 is used to determine the applicability of the high-speed railway soft soil roadbed settlement prediction model under homogeneous natural foundation conditions and homogeneous sand well foundation conditions;

[0195] The confidence analysis module 103 is used to perform confidence analysis on the roadbed settlement prediction based on the simulated settlement observation data, the applicability of the high-speed railway soft soil roadbed settlement prediction model under the homogeneous natural foundation condition and the homogeneous sand well foundation condition.

[0196] The present invention also provides a memory storing a plurality of instructions, wherein the instructions are used to implement the method as in the first embodiment.

[0197] like Fig.19 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301, the memory 302 stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the method as in the first embodiment.

[0198] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A roadbed settlement prediction confidence analysis method, characterized in that: include: S1, generating simulated settlement observation data, wherein the simulated settlement observation data is generated based on discrete observation data with observation errors; S2, determine the applicability of the high-speed railway soft soil roadbed settlement prediction model under homogeneous natural foundation conditions and homogeneous sand well foundation conditions; S3, performing a confidence analysis on the roadbed settlement prediction based on the simulated settlement observation data, the applicability of the high-speed railway soft soil roadbed settlement prediction model under the homogeneous natural foundation condition and the homogeneous sand well foundation condition; The S1 includes: S11, for the settlement problem of soft soil roadbed under different foundation treatment forms and different soil conditions, the corresponding theoretical continuous data under different conditions are calculated based on the homogeneous natural foundation calculation model and the sand well foundation calculation model; S12, dividing the theoretical continuous data into observation period data and late settlement data according to different first settlement observation conditions, and discretizing the observation period data according to second settlement observation conditions to obtain discrete data; S13, superimposing observation errors on discrete observation period data according to different third settlement observation conditions to generate discrete observation data with the observation errors, and generating the simulated settlement observation data based on the discrete observation data with the observation errors; The S2 includes: S21, using the theoretical model of settlement of homogeneous natural foundation and drainage shaft foundation, the theoretical settlement curves under different soil properties and foundation treatment conditions were obtained; S22, processing the settlement theory curve based on commonly used observation conditions in engineering to generate simulated settlement observation data, and using a curve fitting model to predict the simulated settlement observation data; S23, normalizing the soil properties and structural parameters of the homogeneous drainage shaft foundation based on the equivalent permeability coefficient method, comprehensively utilizing the correlation coefficient, relative error and coefficient of variation of the settlement prediction results to determine the applicability evaluation index of the settlement prediction model, and analyzing and obtaining the optimal use conditions of different curve fitting models based on the applicability evaluation index; The S3 includes: S31, based on the discrete observation data with the observation error, using a curve fitting method to perform prediction to obtain a prediction value corresponding to each group of observation data; repeating S31 to obtain multiple prediction values ​​to form a prediction result; S32, performing statistical analysis on the prediction results to obtain a statistical mean value and a standard deviation of the predicted settlement; S33, deriving corresponding influencing rules based on the predicted settlement statistical mean and the predicted settlement standard deviation respectively.

2. A roadbed settlement prediction confidence analysis method according to claim 1, characterized in that: The first settlement observation condition is the observation duration; the second settlement observation condition is the observation frequency; and the third settlement observation condition is the observation accuracy.

3. A roadbed settlement prediction confidence analysis method according to claim 2, characterized in that: The observation accuracy refers to the influence of Class I, Class II, Class III, Class IV and observation accuracy conditions with greater observation error than Class IV in engineering leveling measurement on the settlement prediction confidence, and the error in the deformation point elevation is used as the control index to distinguish the accuracy of leveling measurements at various levels; the observation frequency refers to the number or frequency of observations using the leveling observation method within the observation period; when analyzing the influence of observation frequency on the settlement prediction confidence, the different constant load periods after the completion of roadbed filling are divided into three observation periods: 0 to 3 months after the completion of filling, 4 to 6 months after the completion of filling, and 6 months after the completion of filling, and the influence of the observation frequency in the corresponding observation period on the prediction confidence compared with the current relevant regulations is determined respectively.

4. A roadbed settlement prediction confidence analysis method according to claim 3, characterized in that: The influencing rules include the influence of observation time on the statistical mean of predicted settlement, the influence of observation accuracy on the statistical mean of predicted settlement, the influence of observation frequency on the statistical mean of predicted settlement, the influence of soil conditions on the statistical mean of predicted settlement, the influence of observation time on the standard deviation of predicted settlement, the influence of observation accuracy on the standard deviation of predicted settlement, the influence of observation frequency on the standard deviation of predicted settlement and the influence of soil conditions on the standard deviation of predicted settlement.

5. A roadbed settlement prediction confidence analysis system, used to implement the method described in any one of claims 1 to 4, comprising: A data generation module (101) is used to generate simulated settlement observation data, wherein the simulated settlement observation data is generated based on discrete observation data with observation errors; A prediction model applicability analysis module (102) is used to determine the applicability of a high-speed railway soft soil roadbed settlement prediction model under homogeneous natural foundation conditions and homogeneous sand well foundation conditions; A confidence analysis module (103) is used to perform confidence analysis on the prediction of roadbed settlement based on the simulated settlement observation data, the applicability of the high-speed railway soft soil roadbed settlement prediction model under the homogeneous natural foundation condition and the homogeneous sand well foundation condition.

6. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is used to read the instructions and execute the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the method according to any one of claims 1 to 4.

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