Multi-fractal-based vertical shaft vertical mechanical method construction monitoring system and method

Abstract

The invention relates to a multi-fractal-based vertical shaft vertical mechanical method construction monitoring system and method. The method comprises the following steps: S1, monitoring monitoring data acquired by sensors at joints of all vertical shaft segments, and transmitting the monitoring data to a server for classified storage; s2, performing multiple grouping on the monitoring data in the server, and performing multi-fractal calculation on a strain data sequence in the monitoring data by using an MF-DFA algorithm to obtain a multi-fractal spectrum width parameter and a parameter of a proportion of large and small fluctuations in a waveform, and performing judgment and classification according to the variation trend of the width parameter of the multi-fractal spectrum and the proportion parameter of large and small fluctuations in the waveform to obtain the early warning stage of the current vertical shaft duct piece. According to the method, the deformation of the vertical shaft can be accurately and effectively early warned, early warning information is provided for monitoring and management personnel, and the possibility of accidents is reduced.

Description

Technical Field

[0001] This invention relates to the field of vertical shaft mechanical construction, and in particular to a monitoring system and method for vertical shaft mechanical construction based on multifractals. Background Technology

[0002] Urban rail transit, as an effective way to alleviate urban traffic congestion, is often built in densely populated, bustling urban areas with high traffic demand. However, its construction typically requires the construction of a vertical shaft as an entrance / exit, ventilation shaft, and material transport channel. After the shaft is excavated, the shaft wall is constructed using multiple prefabricated shaft segments. However, during vertical mechanical construction, it inevitably affects the surrounding strata and buildings, which may lead to stratum loss, release of surrounding rock pressure, and changes in the original stratum stress and seepage field, thereby causing displacement and deformation of the soil and rock mass, causing deformation or damage to the shaft segments, and ultimately leading to the destruction of the shaft. Therefore, monitoring and early warning of shaft segment deformation is of great significance to ensuring the safety of the shaft structure and construction personnel.

[0003] In existing technologies, methods for monitoring the deformation of shaft segments using strain data mainly include regression analysis and time series analysis. However, due to the complex and variable geological environment near the shaft, the factors inducing shaft deformation are diverse, resulting in deformation curves exhibiting obvious nonlinear characteristics. Ultimately, this leads to inaccuracies and instability in existing technologies for monitoring shaft deformation. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to overcome the defects or deficiencies of the prior art and provide a method and device for monitoring vertical shaft construction using a multifractal method.

[0005] A vertical mechanical construction monitoring method for shafts based on multifractals includes the following steps: S1: Monitor the data collected by sensors at the joints of each vertical shaft segment and transmit the monitoring data to the server for classification and storage; S2: The monitoring data in the server is grouped into multiple groups and the strain data sequence in the monitoring data is calculated using the MF-DFA algorithm to obtain the multifractal spectrum width parameter and the proportion parameter of the size fluctuation in the waveform. The classification is judged based on the changing trend of the multifractal spectrum width parameter and the proportion parameter of the size fluctuation in the waveform to obtain the current warning stage of the shaft segment.

[0006] Furthermore, step S2 also includes: S22: Based on the quantity of strain data and the multifractal principle, group the strain data in the server according to the time sequence; S23: Perform MF-DFA multifractal calculations separately for each strain data sequence to obtain the multifractal spectrum width parameter. The proportion of large and small fluctuations in the waveform. ; S24: Based on the width parameter of the multifractal spectrum and multifractal spectrum The proportion of large and small fluctuations in the waveform. The changing trend is used to determine the early warning stage of the shaft segments.

[0007] Further, step S23 includes the following steps: S231: Calculate the cumulative deviation sequence of the strain data sequence; S232: By a time scale s The cumulative deviation sequence Divided into m The sub-intervals are obtained , where 1< v ≤ m Each subinterval has s One data point; S233: For the cumulative deviation sequence Each sub-interval Calculate its residual sequence ; S234: Calculate the root mean square error for each subinterval. ; S235: Calculate each subinterval using the sliding window method. The first-order wave function is expressed as follows:

[0008] in, The mean squared error for each subinterval. for The length of the strain data sequence, The number of strain data points in the sub-interval. For each sub-interval Order wave function.

[0009] S236: Through each subinterval First wave function Get a series - Point values ​​that satisfy the following power-law relation:

[0010] Taking the logarithm of both sides of the above equation, we get:

[0011] in, For each sub-interval First-order wave function, For each subinterval, the corresponding generalized Hurst exponent is given. S237: Based on the generalized Hurst exponent corresponding to each subinterval The multifractal spectrum was calculated. Its calculation expression is as follows:

[0012] in: For each subinterval, the corresponding generalized Hurst exponent is given. Renyi Index It is a multifractal spectrum; S238: Based on multifractal spectrum The width parameter of the multifractal spectrum was calculated. and multifractal spectrum The proportion of large and small fluctuations in the waveform. Its calculation expression is as follows:

[0013] in: A larger value indicates a multifractal spectrum. The more drastic the fluctuations; The smaller the value, the more it represents a multifractal spectrum. The larger the proportion of medium to large oscillation waveforms.

[0014] Furthermore, in step S232, a bidirectional partitioning method is adopted, that is, from the cumulative deviation sequence... The division operation begins simultaneously at both the beginning and end of the array, resulting in 2. m Sub-intervals , where 1< v ≤2 m This ensures that all data points are included in at least one sub-interval, preventing data loss.

[0015] Furthermore, in step S22, each group contains 100 strain data points. For the multifractal algorithm, if the amount of strain data in each group is too small, the multifractal characteristics of the strain data will not be obvious, leading to a deviation in the final warning. If the amount of strain data in each group is too large, it will result in excessive computation and reduce the warning efficiency.

[0016] Furthermore, the monitoring data includes strain data and temperature data, and step S2 further includes step S21: numerically correcting the corresponding strain data based on the temperature data in the server.

[0017] A vertical shaft construction monitoring system based on multifractals, comprising: The monitoring module is used to monitor the monitoring data collected by sensors at the joints of each shaft segment and transmit the monitoring data to the server for classification and storage. The early warning calculation module is used to perform multi-grouping of the monitoring data in the server and to perform multifractal calculation on the strain data sequence in the monitoring data using the MF-DFA algorithm to obtain the multifractal spectrum width parameter and the proportion parameter of the size fluctuation in the waveform. Based on the changing trends of the multifractal spectrum width parameter and the proportion parameter of the size fluctuation in the waveform, the module judges and classifies to obtain the early warning stage of the current shaft segment.

[0018] Furthermore, the monitoring module includes several strain sensors, several temperature sensors, optical fiber cables, and a server; The strain sensors are distributed at the joints of different shaft segments; The temperature sensors are arranged one-to-one with the strain sensors. One end of the transmission optical cable is connected to the data connectors of the plurality of strain sensors and the plurality of temperature sensors, and the other end is connected to the server; The server categorizes and stores the strain and temperature data according to different shaft segments, different monitoring points, and different sensors.

[0019] Furthermore, the early warning calculation module includes a data grouping unit, a multifractal unit, and an early warning classification unit; The data grouping unit is used to group the strain data in the server according to the quantity of strain data and the multifractal principle, in chronological order. The multifractal unit is used to perform MF-DFA multifractal calculations on each strain data sequence individually to obtain the multifractal spectrum width parameter and the proportion parameter of large and small fluctuations in the waveform. The early warning classification unit is used to determine the early warning stage of the shaft segment based on the changing trend of the width parameter of the multifractal spectrum and the proportion parameter of the size fluctuation in the multifractal spectrum waveform.

[0020] Furthermore, the early warning calculation module also includes a data correction unit, which is used to perform numerical correction on the corresponding strain data based on the temperature data in the server.

[0021] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the vertical shaft mechanical construction monitoring system based on multifractals according to the present invention. Figure 2 This is a flowchart of the vertical shaft construction monitoring method based on multifractals according to the present invention. Figure 3 This is a flowchart of step S23 in the vertical mechanical construction monitoring method for shafts based on multifractals of the present invention; Figure 4 This is a scatter plot of strain data for shaft segments in one embodiment; Figure 5 As one embodiment q First wave function The trend curve of the double logarithmic fitting; Figure 6 Generalized Hurst exponent in one embodiment picture; Figure 7 Different in one embodiment m Steps Fitting result diagram; Figure 8 This is a time-varying response pattern diagram of multiple fractal features in one embodiment; Figure 9 This is a diagram illustrating the early warning stage division of shaft segments based on multifractals in one embodiment; Figure 10 This is a scatter plot of joint displacement monitoring data for shaft segments in one embodiment. Detailed Implementation

[0023] This invention designs a multifractal-based vertical mechanical construction monitoring system and method for shafts. It collects strain data using sensors installed at the joints of shaft segments; the strain data is divided into multiple groups for multifractal (MF-DFA) calculation to obtain the multifractal spectrum width parameter. And the proportion of large and small fluctuations in the waveform. Based on the multifractal spectrum width parameter And the proportion of large and small fluctuations in the waveform. The current early warning stage of the shaft is determined by classifying the changing trends. This invention can provide accurate and effective early warning of shaft deformation, offering warning information to monitoring and management personnel and reducing the likelihood of accidents.

[0024] Please see Figure 1 and Figure 2 , Figure 1This is a schematic diagram of the structure of a vertical shaft mechanical construction monitoring system based on multifractals according to the present invention. Figure 2 This is a flowchart of the vertical mechanical construction monitoring method for shafts based on multifractals according to the present invention.

[0025] The present invention provides a monitoring system for vertical shaft construction using a multifractal method, comprising a monitoring module 10 and an early warning calculation module 20.

[0026] The monitoring module 10 is used to perform step S1: monitor the monitoring data collected by the sensors installed at the joints of the shaft segments and transmit the monitoring data to the server for classification and storage.

[0027] Specifically, the monitoring module 10 includes several strain sensors 11, several temperature sensors 12, a transmission optical cable 13, and a server 14.

[0028] The strain sensors 11 are dispersedly arranged at the joints of different shaft segments; in this embodiment, the strain sensors 11 are embedded fiber optic grating (GFRP) strain sensors. Since the strain sensors 11 are easily damaged when directly bonded to the outer surface of the shaft segment, they are bonded to the surface of the outer main reinforcement bars inside the steel cage of the shaft segment during the prefabrication process, with the strain sensors 11 positioned 5 cm away from the outer arc surface of the shaft segment.

[0029] The plurality of temperature sensors 12 are arranged one-to-one with the plurality of strain sensors 11 for temperature compensation, compensating for the strain deviation measured by the strain sensors 11 caused by temperature changes, thereby eliminating the influence of temperature on the strain measurement results.

[0030] The strain transfer mechanism between the structure of the shaft segment and the optical fiber of the strain sensor 11 depends on its gauge length and the mechanical properties of the coating material. Therefore, before installation, a tensile test needs to be performed on the optical fiber of the strain sensor 11 attached to the steel reinforcement sample to detect the fit between the steel reinforcement and the optical fiber of the strain sensor 11 and to determine whether the strain transfer effect is good. During actual installation, the surface of the steel reinforcement is first cleaned to ensure good adhesion between the optical fiber and the steel reinforcement. During the installation of the strain sensor 11, the optical fiber of the strain sensor 11 is pre-stretched and temporarily fixed with cable ties and tape. Then, a high shear strength engineering adhesive is used to bond the optical fiber of the strain sensor 11 to the steel reinforcement. After the adhesive cures, the data connector of the strain sensor 11 is extended from inside the steel reinforcement cage and protected. After the concrete is poured and cured and the shaft segment is installed by the tunnel boring machine, the optical fibers of each strain sensor 11 in the shaft segment are welded together by fusion splicing.

[0031] Preferably, when one to three strain sensors 11 that are relatively close to each other are connected and used, the connectors of the strain sensors 11 can be directly connected to the flange.

[0032] One end of the transmission optical cable 13 is connected to the data connector of the plurality of strain sensors 11 and the plurality of temperature sensors 12, and the other end is connected to the server 14 to transmit the monitoring data, namely the strain data of the strain sensor 11 and the temperature data of the temperature sensor 12, to the server 14 for storage.

[0033] The server 14 classifies and stores the strain and temperature data transmitted from the optical fiber cable 13 according to different shaft segments, different monitoring points, and different sensors. At the same time, staff can view and retrieve the monitoring data in real time on the server 13.

[0034] The early warning calculation module 20 is used to execute step S2: to perform multiple grouping of the monitoring data in the server and to perform multifractal calculation on the strain data sequence in the monitoring data using the MF-DFA algorithm to obtain the multifractal spectrum width parameter. And the proportion of large and small fluctuations in the waveform. ,according to and The changing trends are judged and classified to obtain the current early warning stage of the shaft segments.

[0035] The early warning calculation module 20 includes a data correction unit 21, a data grouping unit 22, a multifractal unit 23, and an early warning classification unit 24.

[0036] The data correction unit 21 is used to perform step S21: to perform numerical correction on the corresponding strain data based on the temperature data in the server, so as to avoid the influence of temperature difference on the strain data measurement results.

[0037] The data grouping unit 22 is used to perform step S22: group the strain data in the server according to the quantity of strain data and the multifractal principle, in chronological order.

[0038] For multifractal algorithms, if the amount of strain data in each group is too small, the multifractal characteristics of the strain data will not be obvious, leading to deviations in the final warning. If the amount of strain data in each group is too large, the computational load will be too high, reducing the warning efficiency. In this embodiment, the amount of strain data in each group is selected to be 100, which constitute the strain data sequence of that group.

[0039] Please see Figure 3 The multifractal unit 23 is used to perform step S23: perform MF-DFA multifractal calculations on each strain data sequence individually to obtain the multifractal spectrum width parameter. The proportion of large and small fluctuations in the waveform. Specifically, it includes the following steps: S231: Calculate the cumulative deviation sequence of the strain data sequence.

[0040] Specifically, let each set of strain data sequences be... The length of the strain data sequence is The mean of the strain data sequence is ,but Compared to Cumulative deviation sequence The calculation expression is:

[0041] in, For strain data sequence The arithmetic mean, t Less than or equal to N .

[0042] S232: By a time scale s The cumulative deviation sequence Divided into m The sub-intervals are obtained ,in 1<v≤ m Each subinterval has s Data.

[0043] Specifically, a time scale is set. , cumulative deviation sequence Divided into Divide into equal parts, so that it has Sub-intervals, .

[0044] Because during the above calculation of the cumulative deviation sequence It may not be possible to be Divisible. Therefore, in this embodiment, a bidirectional partitioning method is adopted, that is, the partitioning operation starts simultaneously from both the beginning and end of the cumulative deviation sequence to obtain... Sub-intervals , of which 1 <v≤2 m This ensures that all data points are included in at least one sub-interval, preventing data loss.

[0045] S233: For the cumulative deviation sequence Each sub-interval Calculate its residual sequence Its calculation expression is:

[0046] in, For sub-intervals; For the first Sub-intervals Order-fit polynomial. The range of values ​​is [1, 2]. ], t The value range is [1, ...]. ].

[0047] S234: Calculate the root mean square error for each subinterval. :

[0048] in, For each sub-interval, the residual sequence is... The number of strain data points in the sub-interval. is the mean squared error for each subinterval.

[0049] S235: Calculate each subinterval using the sliding window method. The first-order wave function is expressed as follows:

[0050] in, The mean squared error for each subinterval. The number of subintervals The number of strain data points in the sub-interval. For each sub-interval Order wave function.

[0051] S236: Through each subinterval First wave function Get a series - Point values ​​that satisfy the following power-law relation:

[0052] Taking the logarithm of both sides of the above equation, we get:

[0053] in: For each sub-interval First-order wave function; The generalized Hurst exponent for each subinterval can be calculated using the above formula; It is a constant coefficient.

[0054] S237: Based on the generalized Hurst exponent corresponding to each subinterval The multifractal spectrum was calculated. Its calculation expression is as follows:

[0055] in: For each subinterval, the corresponding generalized Hurst exponent is given. Renyi Index It is a multifractal spectrum.

[0056] S238: Based on multifractal spectrum The width parameter of the multifractal spectrum was calculated. and multifractal spectrum The proportion of large and small fluctuations in the waveform. Its calculation expression is as follows:

[0057] in: A larger value indicates a multifractal spectrum. The more drastic the fluctuations; The smaller the value, the more it represents a multifractal spectrum. The larger the proportion of medium to large oscillation waveforms.

[0058] The early warning classification unit 24 is used to execute step S24; S24: Based on the width parameter of the multifractal spectrum and multifractal spectrum The proportion of large and small fluctuations in the waveform. The changing trend is used to determine the early warning stage of the shaft segments.

[0059] Preferably, taking one embodiment as an example, please refer to Figure 4 Strain data from a single shaft over 188 consecutive days were selected, and some data with large deviations were removed, resulting in a total of 750 sets of strain data.

[0060] Specifically, to obtain the best computational results, it is necessary to experiment with and preset the parameters for different data. In this embodiment, the multifractal preset parameters obtained after experimentation are as follows: , , , ;in Represents a time scale s The minimum value, Represents a time scale s The maximum value, q This represents the order of the fluctuation function for each subinterval. M Represents calculation The fitting order of the relation. Each preset parameter is determined by the following method: For time scale s Please see Figure 5 From 750 sets of strain data, 100 sets of strain data were selected, and their values ​​were calculated. q First wave function The double logarithmic fitted trend curve; where the preset parameters are... m Set to 2, q The value range is set to [-10, 10], resulting in a total of 101 curves. From... Figure 5 It is obvious that when When the parameters are determined, the curve fits well. , .

[0061] The order of the wave function for each subinterval q Please see Figure 6 Four strain data sets were selected from 750 sets of strain data to obtain the generalized Hurst exponents corresponding to the four sets of strain data. The graph, which presents itself as a curve with a specific pattern of change, shows that when it is in [- q,q If the value of can fully reflect the trend of the curve within its range, then it is . The optimal value for . Therefore, take . .

[0062] For the order of fitting M (M+2≤s) Please see Figure 7 From 750 sets of strain data, 100 sets of strain data were selected, and different strain values ​​were calculated respectively. M Steps The relationship shows that when M=1, the contour lines are distributed haphazardly and fluctuate wildly. This can lead to mutations and poor fitting results; when M When =2, the contour lines are smoothly and continuously distributed. ， The fitting effect is good. Considering the large amount of data and computation time, the fitting order was selected. M =2.

[0063] Based on the multifractal parameters specified above, perform multifractal analysis on the strain data. Please refer to [link to relevant documentation]. Figure 8 The dataset was divided into 650 groups of 100 data points each, and the multifractal features of each group were calculated. and As its characteristic value, the variation law of the multifractal spectrum parameters of the shaft segment strain data is obtained. For details, please refer to [link to relevant documentation]. Figure 9 In the initial stage of shaft segment deformation, It will show an increasing trend. The deformation will show a decreasing trend, which can be regarded as a precursor to deformation warning; in the middle stage of shaft segment deformation, It will show a slow downward trend. Fluctuations will occur, which can be considered a stable period for deformation warning; in the later stage of shaft segment deformation, It will show a sharp upward trend. It will also show an upward trend, which can be regarded as a sudden change period for deformation warning.

[0064] Preferably, to verify the accuracy of judging the deformation stage of shaft segments by the change of multifractal spectrum parameters of shaft segment strain data, the joint displacement monitoring data of the same shaft segment is used to measure the deformation of the shaft segment, and the data is compared with the deformation stage of shaft segments judged by the change of multifractal spectrum parameters of shaft segment strain data.

[0065] Please see Figure 10 It can be seen that the joint width of the tunnel segments increased from 0.0094m to 0.0100m in the first 15 days, indicating that the deformation of the tunnel segments was in a slow growth state, i.e., in the precursor stage. On day 32, a sudden change occurred in the joint width, reducing it by 98.5%. From day 15 to day 135, although the joint width of the tunnel segments changed, it remained generally stable, i.e., in the stable period. From day 136 to day 163, the joint width of the tunnel segments fluctuated drastically, indicating the abrupt change period. This conclusion is basically consistent with the method of judging the deformation stage of shaft tunnel segments by changes in the multifractal spectrum parameters of the strain data, indicating that judging the deformation stage of shaft tunnel segments by changes in the multifractal spectrum parameters of the strain data has high accuracy.

[0066] Compared to existing methods that monitor shaft segment deformation using strain data, the multifractal-based vertical mechanical construction monitoring system and method of this invention collects strain data using sensors installed at the joints of shaft segments. The strain data is divided into multiple groups for multifractal (MF-DFA) calculations to obtain the multifractal spectrum width parameter and the proportion of magnitude fluctuations in the waveform. Based on the changing trends of these parameters, the current early warning stage of the shaft is determined. Data verification shows that this invention can provide accurate and effective early warnings of shaft deformation, offering warning information to monitoring and management personnel and reducing the likelihood of accidents.

[0067] Based on the same inventive concept, this application also provides an electronic device, which can be a server, desktop computing device, or mobile computing device (e.g., laptop computing device, handheld computing device, tablet computer, netbook, etc.) or other terminal device. The device includes one or more processors and a memory, wherein the processor is used to execute programs to implement embodiments of the present invention; and the memory is used to store computer programs executable by the processor.

[0068] Based on the same inventive concept, this application also provides a computer-readable storage medium corresponding to the aforementioned embodiments of the multifractal-based vertical shaft mechanical construction monitoring method. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the multifractal-based vertical shaft mechanical construction monitoring method described in any of the above embodiments.

[0069] This application may take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code. Computer storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0070] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and the present invention also intends to include these modifications and variations.

Claims

1. A method for monitoring construction of a vertical shaft by a vertical mechanical method based on multi-fractals, characterized in that: The method comprises the following steps: S1: monitoring the monitoring data collected by the sensors at the joint of each shaft segment and transmitting the monitoring data to a server for classified storage; S2: performing multiple grouping on the monitoring data in the server and performing multiple fractal calculation on the strain data sequence in the monitoring data by using the MF-DFA algorithm to obtain a multiple fractal spectrum width parameter and a proportion parameter of large and small fluctuations in a waveform, and judging and classifying according to the change trend of the multiple fractal spectrum width parameter and the proportion parameter of large and small fluctuations in the waveform to obtain a warning stage of the current shaft segment.

2. The multi-fractal based monitoring method of construction of shaft vertical mechanical method according to claim 1, characterized in that: The step S2 further comprises: S22: grouping the strain data in the server in time sequence according to the number of strain data and the multiple fractal principle; S23: MF-DFA multifractal calculation is performed on each set of strain data sequences separately to obtain a multifractal spectrum width parameter , a parameter of proportion of large fluctuations in the waveform ; S24: judging the early-warning stage of the shaft segment according to the change trend of the width parameter of the multifractal spectrum , and the multifractal spectrum of the wave shape, and the proportion parameter of the size fluctuation in the wave shape.

3. The multi-fractal based monitoring method of construction of shaft vertical mechanical method according to claim 2, characterized in that: The step S23 comprises the following steps: S231: calculating an accumulated deviation sequence of the strain data sequence; S232: By a time scale s The cumulative deviation sequence Divided into m The sub-intervals are obtained , where 1< v ≤ m Each subinterval has s One data point; S233: compute the cumulative deviation sequence for each subinterval of the cumulative deviation sequence and compute its residual sequence ; S234: Calculate the mean square error for each sub-interval ; S235: Calculate the value of each sub-interval by the sliding window method The wavelet function, whose calculation expression is: wherein, is the mean square error for each sub-interval, is the length of the strain data sequence, is the number of strain data in the sub-interval, is the mean square error for each sub-interval, is the mean square error for each sub-interval, S236: passing through each sub-interval of step wave function a series of - point values that satisfy a power law relationship under Taking the logarithm of both sides of the above formula, the following formula is obtained: wherein, is the generalized Hurst exponent for each subinterval, is the generalized Hurst exponent for each subinterval,​ S237: the generalized Hurst index corresponding to each subinterval The multifractal spectrum is calculated The calculation expression is as follows: wherein: Hurst exponent for each subinterval, Renyi exponent, multifractal spectrum; S238: calculating the width parameter of the multifractal spectrum S240: calculating the proportion parameter of the multifractal spectrum S242: calculating the proportion parameter of the multifractal spectrum S244: calculating the proportion parameter of the multifractal spectrum S246: calculating the proportion parameter of the multifractal spectrum in: A larger value indicates a multifractal spectrum. The more drastic the fluctuations; The smaller the value, the more it represents a multifractal spectrum. The larger the proportion of medium to large oscillation waveforms.

4. The multi-fractal based monitoring method of construction of shaft vertical mechanical method according to claim 3, characterized in that: In step S232, a forward and backward bidirectional division method is adopted, that is, the division operation is started from the head and tail of the cumulative deviation sequence , and two m sub-intervals are obtained, where 1 v ≤2 m .

5. The multi-fractal based monitoring method of construction of shaft vertical mechanical method according to claim 4, characterized in that: In the step S22, the amount of strain data of each group is 100.

6. The multi-fractal based monitoring method of construction of shaft vertical mechanical method according to any one of claims 1 to 5, characterized in that: The monitoring data comprises strain data and temperature data, and the step S2 further comprises a step S21 of correcting the value of the strain data corresponding to the temperature data in the server.

7. A multi-fractal based shaft vertical mechanical method construction monitoring system characterized by: It comprises: a monitoring module for monitoring the monitoring data collected by the sensors at the joint of each shaft segment and transmitting the monitoring data to a server for classified storage; a warning calculation module for performing multiple grouping on the monitoring data in the server and performing multiple fractal calculation on the strain data sequence in the monitoring data by using the MF-DFA algorithm to obtain a multiple fractal spectrum width parameter and a proportion parameter of large and small fluctuations in a waveform, and judging and classifying according to the change trend of the multiple fractal spectrum width parameter and the proportion parameter of large and small fluctuations in the waveform to obtain a warning stage of the current shaft segment.

8. The multi-fractal based shaft vertical construction monitoring system of claim 7, wherein: The monitoring module comprises a plurality of strain sensors, a plurality of temperature sensors, a transmission optical cable and a server; The plurality of strain sensors are dispersedly arranged at the joints of different shaft segments; The plurality of temperature sensors are correspondingly arranged near the plurality of strain sensors; One end of the transmission optical cable is connected with the data connectors of the plurality of strain sensors and the plurality of temperature sensors, and the other end is connected with the server; The server classifies and stores the strain data and the temperature data according to different shaft segments, different monitoring points and different sensor categories.

9. The multi-fractal based construction monitoring system for shaft vertical mechanical method according to claim 7 or 8, characterized in that: The warning calculation module comprises a data grouping unit, a multiple fractal unit and a warning classification unit; The data grouping unit is used for grouping the strain data in the server in time sequence according to the number of strain data and the multiple fractal principle; The multi-fractal unit is used for performing MF-DFA multi-fractal calculation on each group of strain data sequences to obtain multi-fractal spectrum width parameters , a proportion parameter of large fluctuations in the waveform ; The pre-warning classification unit is used to determine the pre-warning stage of the shaft segment according to the change trend of the proportion parameter of the size fluctuation in the multi-fractal spectrum waveform . The pre-warning classification unit is used to determine the pre-warning stage of the shaft segment according to the change trend of the proportion parameter of the size fluctuation in the multi-fractal spectrum waveform.

10. The multi-fractal based shaft vertical construction monitoring system of claim 9, wherein: The warning calculation module further comprises a data correction unit for correcting the value of the strain data corresponding to the temperature data in the server.

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