Multi-base-station deformation monitoring data processing method

Abstract

The invention discloses a multi-base-station deformation monitoring data processing method, and relates to the technical field of deformation monitoring, and the method specifically comprises the steps: 1, analyzing a deformation monitoring object, determining a deformation state, and dividing data types according to the deformation monitoring content; 2, noise reduction processing is carried out on the data of multi-base-station deformation monitoring in combination with different noise reduction methods; 3, determining standard state data of the deformation monitoring object based on the structure and material state of the deformation monitoring object; 4, based on the standard state data, analyzing deformation data of multi-base-station deformation monitoring in a current time period, and judging whether a deformation monitoring trend is controllable or not; noise reduction processing is carried out by combining a local mean decomposition method and an empirical mode decomposition method, the influence degree of noise signals on deformation monitoring data is further reduced, layout and number optimization of multiple base stations are achieved through comprehensive analysis of the deformation trend, and comprehensive coverage of a monitoring area and data accuracy are ensured.

Description

Technical Field

[0001] The invention belongs to the technical field of deformation monitoring, and in particular relates to a multi-base station deformation monitoring data processing method. Background Art

[0002] With the continuous advancement of GNSS technology, especially the rapid development of my country's Beidou navigation and positioning system, the fusion positioning technology of multi-frequency, multi-mode and multi-satellite systems has become a hot topic in industry research. GNSS technology has the advantages of high precision, all-weather and automation, providing strong technical support for deformation monitoring.

[0003] The traditional single-base station monitoring system has the problem of benchmark stability error transmission, which easily leads to deviation or even distortion of deformation information. The multi-base station monitoring system can increase the number of baselines and redundant observations by building a monitoring network of multiple base stations, thereby improving the redundancy and reliability of monitoring data. At the same time, multiple base stations can check each other to further reduce errors and improve monitoring accuracy.

[0004] There are various noise sources inside the GNSS receiver, such as thermal noise, quantization noise, etc. These noises will affect the signal processing capability of the receiver and reduce the accuracy of deformation monitoring data.

[0005] Deformation monitoring data often involves multiple aspects of information (such as displacement, velocity, acceleration, etc.), which requires the comprehensive analysis of multiple analysis methods. However, existing data processing methods often focus on single-aspect analysis and lack comprehensive analysis capabilities. Summary of the invention

[0006] The object of the present invention is to provide a multi-base station deformation monitoring data processing method for solving the technical problems in the prior art that deformation monitoring data is affected by noise and lacks comprehensive analysis capability.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] Step 1: Analyze the object of deformation monitoring, clarify the deformation state, and divide the data type according to the deformation monitoring content; analyze the structural type and material type of the deformation monitoring object, refer to the relevant data of the existing deformation monitoring object, combine structural mechanics and materials science, and determine the structural strength and material strength of the deformation monitoring object through theoretical formulas. Combined with relevant historical deformation data, the deformation state of the deformation monitoring object is divided into normal deformation state and abnormal deformation state. The deformation trend of the deformation monitoring object is determined through the content of deformation monitoring. The design standards, historical data or natural laws of the deformation monitoring object are comprehensively considered to determine whether the current deformation trend is within a controllable range. The normal deformation state refers to the deformation degree within a certain period of time according to the current deformation trend. The abnormal deformation state refers to the deformation degree within a certain period of time according to the current deformation trend. It is not within the controllable range. The deformation monitoring content is divided into different data types including: deformation data and environmental data.

[0009] Step 2: Denoise the data of deformation monitoring of multiple base stations by combining different noise reduction methods; collect deformation monitoring data of each base station through sensor networks or Internet of Things devices, record the current unprocessed deformation monitoring data as original monitoring data, the original monitoring data contains a large amount of noise data, and it is necessary to perform noise reduction on the original monitoring data, decompose the data signal to be denoised by the local mean decomposition method, obtain several components represented by high-frequency product functions, retain the low-frequency components, and use the high-frequency components as the signals to be denoised. The method for judging high-frequency and low-frequency components is as follows:

[0010] The signal component in the time domain is converted to the frequency domain through Fourier transformation, and the frequency w and the power spectrum density function P(w) of the signal component are calculated to determine that the low frequency band range is between a and b Hz. In this embodiment, 0 to 100 Hz is set as the low frequency band range, and the power spectrum data is extracted within the determined low frequency band range to determine the linear relationship between the power spectrum density and the frequency within the low frequency band range. The specific formula is as follows:

[0011] logP(w)=αlogw+β+o(w);

[0012] Wherein α represents the slope of the linear relationship, β represents the offset constant of the power spectrum density changing with frequency, and in this embodiment, β is set to 1, o(w) represents the fitting function, which represents the discontinuous function that makes the linear relationship between the power spectrum density and the frequency hold, and the objective function of the fitting function is set. The objective function of the fitting function is specifically as follows:

[0013]

[0014] Determine the true value of α through the objective function of the fitting function and set the low and high frequency cutoff value when When the signal is judged as high frequency, otherwise the signal is judged as low frequency. It is determined according to specific circumstances and needs, and is usually jointly formulated and confirmed by professionals or relevant stakeholders. In this embodiment, 0 to 100 Hz is set as the low frequency range, and the low and high frequency boundary value is set. is 1.

[0015] The signal to be denoised is determined by judging the low-frequency and high-frequency components. For each signal to be denoised, in order to avoid mode aliasing, two white noise signals of the same size and opposite positive and negative relationships are added respectively to obtain two signal components to be denoised. For the signal components to be denoised, the empirical mode decomposition method is used to perform further denoising to obtain the IMF component. The permutation entropy of the IMF component under multiple degrees is determined according to the multi-scale permutation entropy criterion. The normalized permutation entropy of the IMF component is determined by setting the scale factor, the delay factor and the embedding factor. In this embodiment, the scale factor, the delay factor and the embedding factor are set to 16, 1 and 6 respectively. The permutation entropy threshold f is set, and f is determined according to specific circumstances and needs. The IMF component with a permutation entropy value greater than or equal to f is judged as a high-frequency component, otherwise it is a low-frequency component. The low-frequency component is retained and the high-frequency component is screened out. The local mean decomposition method is combined with the low-frequency component obtained by the empirical mode decomposition method to obtain complete data with denoising. The data content after denoising is more accurate and is recorded as denoised data, and the denoised data is normalized.

[0016] Step 3: Determine the standard state data of the deformation monitoring object based on the structure and material state of the deformation monitoring object; collect the detailed structure and material state of the deformation monitoring object, simulate the deformation monitoring object, construct a physical model with the structure of the deformation monitoring object, use finite element analysis and discrete element analysis methods to establish a numerical model of the deformation monitoring object, solve the numerical model according to the actual material properties and geometric dimensions of the deformation monitoring object, and obtain an initial simulation object model; set the simulation environment parameters according to the real environment, conduct simulation experiments with a period of time t, and use various deformation data as input of the simulation model. The various deformation data include: the displacement change rate, the tilt change rate, the vibration amplitude change rate and the structural damage area change rate within the period of time t. Simulate n cycles according to the input of the simulation model, output the deformation degree of the deformation monitoring object in n cycles, judge whether the input of the simulation model affects the normal deformation state based on the deformation degree, and comprehensively consider the design standards, historical data or natural laws of the deformation monitoring object to determine the deformation data range in which the abnormal deformation state will not occur, and take the maximum value boundary of each type of deformation data range as the standard state data of the corresponding type. In this embodiment, n is 5.

[0017] Step 4: Based on the standard state data, analyze the deformation data of the deformation monitoring of multiple base stations in the current time period to determine whether the deformation monitoring trend is controllable; specifically, obtain the displacement change rate d of each base station in the current time period, compare the standard state data of the displacement change rate, and obtain the displacement change ratio D;

[0018] Obtain the tilt change rate v in the current time period, compare it with the standard state data of the tilt change rate, and obtain the tilt change ratio V;

[0019] Obtain the vibration amplitude change rate q within the current time period, compare it with the standard state data of the vibration amplitude change rate, and obtain the vibration amplitude change ratio Q;

[0020] Obtain the structural damage area change rate s within the current time period, compare it with the standard state data of the structural damage area change rate, and obtain the structural damage area change ratio S;

[0021] The displacement change ratio D, the tilt change ratio V, the vibration amplitude change ratio Q, and the structural damage area change ratio S are used as inputs of the real-time deformation trend assessment model. The real-time deformation trend assessment model is obtained according to the deformation trend assessment formula. The deformation trend assessment formula is as follows:

[0022]

[0023] Wherein Fa represents the deformation trend assessment value, H represents the structural strength of the deformation monitoring object before the current cycle, U represents the material strength of the deformation monitoring object, k1, k2, k3, and k4 are weight coefficients, which are determined according to the influence of the deformation data type on the deformation trend, and are usually jointly formulated and confirmed by professionals or relevant stakeholders, and k1+k2+k3+k4=1. In this embodiment, k1=0.2, k2=0.3, k3=0.1, and k4=0.4 are set.

[0024] The deformation trend evaluation value of each base station is integrated, and its average value Fa′ is taken. Based on the simulation experiment of the deformation monitoring object, the deformation trend evaluation threshold I is determined. I is the maximum deformation trend evaluation value that keeps the deformation monitoring object in a normal deformation state. When Fa′ is less than or equal to I, it is judged that the deformation monitoring object is in a normal deformation state within n cycles, that is, the deformation trend within n cycles is within a controllable range. Conversely, it is judged that the deformation monitoring object is in an abnormal deformation state within n cycles, that is, the deformation trend within n cycles is not within a controllable range. The deformation trend difference coefficient E is set, and the deformation trend evaluation value of a certain base station is subtracted from Fa′ and the average value is taken to obtain F. When F is greater than E, the monitoring equipment of the base station is checked, and the layout of the base station is optimized according to the monitoring requirements and on-site conditions to ensure the comprehensive coverage of the monitoring area and the accuracy of the data. E is determined according to the actual situation and needs, and is usually jointly formulated and confirmed by professionals or relevant stakeholders. In this embodiment, E is equal to

[0025] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0026] 1. The present invention combines the local mean decomposition method with the empirical mode decomposition method to perform noise reduction processing, thereby further reducing the influence of the noise signal on the deformation monitoring data. By adding a white noise signal to the signal to be denoised, the mode aliasing phenomenon that may be generated when the two noise reduction methods are combined is avoided;

[0027] 2. The present invention evaluates deformation trends by integrating deformation monitoring data and standard state data, and can more intuitively analyze the deformation state of the deformation monitoring object. By comprehensively analyzing the deformation trend evaluation values ​​of multiple base stations, the layout and number of base stations can be optimized to ensure comprehensive coverage of the monitoring area and accurate data. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only 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.

[0029] Figure 1 A step diagram of a multi-base station deformation monitoring data processing method is shown;

[0030] Figure 2 A flow chart of a method for noise reduction by combining the local mean decomposition method with the empirical mode decomposition method is shown. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0032] like Figure 1 , Figure 2 As shown, a multi-base station deformation monitoring data processing method proposed in this embodiment includes the following steps:

[0033] Step 1: Analyze the object of deformation monitoring, clarify the deformation state, and divide the data type according to the deformation monitoring content; analyze the structural type and material type of the deformation monitoring object, refer to the relevant data of the existing deformation monitoring object, combine structural mechanics and materials science, and determine the structural strength and material strength of the deformation monitoring object through theoretical formulas. Combined with relevant historical deformation data, the deformation state of the deformation monitoring object is divided into normal deformation state and abnormal deformation state. The deformation trend of the deformation monitoring object is determined through the content of deformation monitoring. The design standards, historical data or natural laws of the deformation monitoring object are comprehensively considered to determine whether the current deformation trend is within a controllable range. The normal deformation state refers to the deformation degree within a certain period of time according to the current deformation trend. The abnormal deformation state refers to the deformation degree within a certain period of time according to the current deformation trend. It is not within the controllable range. The deformation monitoring content is divided into different data types including: deformation data and environmental data.

[0034] Step 2: Denoise the data of deformation monitoring of multiple base stations by combining different noise reduction methods; collect deformation monitoring data of each base station through sensor networks or Internet of Things devices, record the current unprocessed deformation monitoring data as original monitoring data, the original monitoring data contains a large amount of noise data, and it is necessary to perform noise reduction on the original monitoring data, decompose the data signal to be denoised by the local mean decomposition method, obtain several components represented by high-frequency product functions, retain the low-frequency components, and use the high-frequency components as the signals to be denoised. The method for judging high-frequency and low-frequency components is as follows:

[0035] The signal component in the time domain is converted to the frequency domain through Fourier transformation, and the frequency w and the power spectrum density function P(w) of the signal component are calculated to determine that the low frequency band range is between a and b Hz. In this embodiment, 0 to 100 Hz is set as the low frequency band range, and the power spectrum data is extracted within the determined low frequency band range to determine the linear relationship between the power spectrum density and the frequency within the low frequency band range. The specific formula is as follows:

[0036] logP(w)=αlogw+β+o(w);

[0037] Wherein α represents the slope of the linear relationship, β represents the offset constant of the power spectrum density changing with frequency, and in this embodiment, β is set to 1, o(w) represents the fitting function, which represents the discontinuous function that makes the linear relationship between the power spectrum density and the frequency hold, and the objective function of the fitting function is set. The objective function of the fitting function is specifically as follows:

[0038]

[0039] Determine the true value of α through the objective function of the fitting function and set the low and high frequency cutoff value when When the signal is judged as high frequency, otherwise the signal is judged as low frequency. It is determined according to specific circumstances and needs, and is usually jointly formulated and confirmed by professionals or relevant stakeholders. In this embodiment, 0 to 100 Hz is set as the low frequency range, and the low and high frequency boundary value is set. is 1.

[0040] The signal to be denoised is determined by judging the low-frequency and high-frequency components. For each signal to be denoised, in order to avoid mode aliasing, two white noise signals of the same size and opposite positive and negative relationships are added respectively to obtain two signal components to be denoised. For the signal components to be denoised, the empirical mode decomposition method is used to perform further denoising to obtain the IMF component. The permutation entropy of the IMF component under multiple degrees is determined according to the multi-scale permutation entropy criterion. The normalized permutation entropy of the IMF component is determined by setting the scale factor, the delay factor and the embedding factor. In this embodiment, the scale factor, the delay factor and the embedding factor are set to 16, 1 and 6 respectively. The permutation entropy threshold f is set, and f is determined according to specific circumstances and needs. The IMF component with a permutation entropy value greater than or equal to f is judged as a high-frequency component, otherwise it is a low-frequency component. The low-frequency component is retained and the high-frequency component is screened out. The local mean decomposition method is combined with the low-frequency component obtained by the empirical mode decomposition method to obtain complete data with denoising. The data content after denoising is more accurate and is recorded as denoised data, and the denoised data is normalized.

[0041] Step 3: Determine the standard state data of the deformation monitoring object based on the structure and material state of the deformation monitoring object; collect the detailed structure and material state of the deformation monitoring object, simulate the deformation monitoring object, construct a physical model with the structure of the deformation monitoring object, use finite element analysis and discrete element analysis methods to establish a numerical model of the deformation monitoring object, solve the numerical model according to the actual material properties and geometric dimensions of the deformation monitoring object, and obtain an initial simulation object model; set the simulation environment parameters according to the real environment, conduct simulation experiments with a period of time t, and use various deformation data as input of the simulation model. The various deformation data include: the displacement change rate, the tilt change rate, the vibration amplitude change rate and the structural damage area change rate within the period of time t. Simulate n cycles according to the input of the simulation model, output the deformation degree of the deformation monitoring object in n cycles, judge whether the input of the simulation model affects the normal deformation state based on the deformation degree, and comprehensively consider the design standards, historical data or natural laws of the deformation monitoring object to determine the deformation data range in which the abnormal deformation state will not occur, and take the maximum value boundary of each type of deformation data range as the standard state data of the corresponding type. In this embodiment, n is 5.

[0042] Step 4: Based on the standard state data, analyze the deformation data of the deformation monitoring of multiple base stations in the current time period to determine whether the deformation monitoring trend is controllable; specifically, obtain the displacement change rate d of each base station in the current time period, compare the standard state data of the displacement change rate, and obtain the displacement change ratio D;

[0043] Obtain the tilt change rate v in the current time period, compare it with the standard state data of the tilt change rate, and obtain the tilt change ratio V;

[0044] Obtain the vibration amplitude change rate q within the current time period, compare it with the standard state data of the vibration amplitude change rate, and obtain the vibration amplitude change ratio Q;

[0045] Obtain the structural damage area change rate s within the current time period, compare it with the standard state data of the structural damage area change rate, and obtain the structural damage area change ratio S;

[0046] The displacement change ratio D, the tilt change ratio V, the vibration amplitude change ratio Q, and the structural damage area change ratio S are used as inputs of the real-time deformation trend assessment model. The real-time deformation trend assessment model is obtained according to the deformation trend assessment formula. The deformation trend assessment formula is as follows:

[0047]

[0048] Wherein Fa represents the deformation trend assessment value, H represents the structural strength of the deformation monitoring object before the current cycle, U represents the material strength of the deformation monitoring object, k1, k2, k3, and k4 are weight coefficients, which are determined according to the influence of the deformation data type on the deformation trend, and are usually jointly formulated and confirmed by professionals or relevant stakeholders, and k1+k2+k3+k4=1. In this embodiment, k1=0.2, k2=0.3, k3=0.1, and k4=0.4 are set.

[0049] The deformation trend evaluation value of each base station is integrated, and its average value Fa′ is taken. Based on the simulation experiment of the deformation monitoring object, the deformation trend evaluation threshold I is determined. I is the maximum deformation trend evaluation value that keeps the deformation monitoring object in a normal deformation state. When Fa′ is less than or equal to I, it is judged that the deformation monitoring object is in a normal deformation state within n cycles, that is, the deformation trend within n cycles is within a controllable range. Conversely, it is judged that the deformation monitoring object is in an abnormal deformation state within n cycles, that is, the deformation trend within n cycles is not within a controllable range. The deformation trend difference coefficient E is set, and the deformation trend evaluation value of a certain base station is subtracted from Fa′ and the average value is taken to obtain F. When F is greater than E, the monitoring equipment of the base station is checked, and the layout of the base station is optimized according to the monitoring requirements and on-site conditions to ensure the comprehensive coverage of the monitoring area and the accuracy of the data. E is determined according to the actual situation and needs, and is usually jointly formulated and confirmed by professionals or relevant stakeholders. In this embodiment, E is equal to

[0050] In order to ensure the accuracy of deformation monitoring data, the present invention performs noise reduction processing on the collected data. The noise reduction processing is performed by combining the local mean decomposition method with the empirical mode decomposition method to more effectively remove the noise components in the signal, thereby ensuring the accuracy of the deformation monitoring data. The deformation trend is evaluated by integrating the deformation monitoring data and the standard state data, and the deformation state of the deformation monitoring object can be analyzed more intuitively. By integrating the deformation trend evaluation values ​​of multiple base stations, the layout and number of base stations are optimized, thereby ensuring comprehensive coverage of the monitoring area and accurate data.

[0051] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0052] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for processing deformation monitoring data of multiple base stations, characterized in that: include: Step 1: Analyze the object of deformation monitoring, clarify the deformation state, and divide the data type according to the deformation monitoring content; Step 2: Denoise the data of deformation monitoring of multiple base stations by combining different noise reduction methods; Combining the local mean decomposition method with the empirical mode decomposition method, the data of deformation monitoring of multiple base stations are subjected to denoising. The low-frequency components obtained by the local mean decomposition method and the empirical mode decomposition method are combined to obtain complete data with denoising, which is recorded as denoised data, and the denoised data is normalized. Step 3: Determine standard state data of the deformation monitoring object based on the structure and material state of the deformation monitoring object; Through the simulation experiment on the deformation monitoring object, various types of deformation data are integrated to determine the standard state data corresponding to different types of deformation data; Step 4: Based on the standard state data, analyze the deformation data of the multi-base station deformation monitoring in the current time period to determine whether the deformation monitoring trend is controllable; The standard status data and the deformation data of the base station in the current time period are combined to obtain the deformation trend evaluation value corresponding to each base station, and the deformation trend evaluation threshold is determined. The average deformation trend evaluation value of all base stations is calculated, and the average value is compared with the deformation trend evaluation threshold to determine the deformation status of the deformation monitoring object in the time period. By setting the deformation trend difference coefficient, the layout setting of multiple base stations is optimized.

2. A method for processing deformation monitoring data of multiple base stations according to claim 1, characterized in that: Analyze the object of deformation monitoring and clarify the deformation state. The specific methods are as follows: Analyze the structural type and material type of the deformation monitoring object, refer to the relevant data of the existing deformation monitoring objects, combine structural mechanics and materials science, determine the structural strength and material strength of the deformation monitoring object through theoretical formulas, and determine the deformation state of the deformation monitoring object in combination with relevant historical deformation data. First, determine the deformation trend of the deformation monitoring object through the content of deformation monitoring, and comprehensively determine whether the current deformation trend is within a controllable range based on the design standards, historical data or natural laws of the deformation monitoring object. The normal deformation state refers to the development of the current deformation trend, and the deformation degree is within a controllable range within a certain period of time. The abnormal deformation state refers to the development of the current deformation trend, and the deformation degree is not within a controllable range within a certain period of time.

3. A method for processing deformation monitoring data of multiple base stations according to claim 1, characterized in that: Combining the local mean decomposition method with the empirical mode decomposition method, the data of deformation monitoring of multiple base stations are subjected to noise reduction. The specific method is as follows: The unprocessed deformation monitoring data is recorded as the original monitoring data. The original monitoring data is decomposed by the local mean decomposition method to obtain several signal components represented by high-frequency product functions. The high-frequency and low-frequency components are judged, and the low-frequency components are retained. The high-frequency components are used as the signals to be denoised. For the signals to be denoised, two white noise signals of the same size and opposite positive and negative relationships are added to obtain two signal components to be denoised. Then, the empirical mode decomposition method is used for further denoising to obtain the imf component. The low-frequency components in the imf component are screened, and the low-frequency components obtained by the local mean decomposition method and the empirical mode decomposition method are combined to obtain the complete data with denoising.

4. A method for processing deformation monitoring data of multiple base stations according to claim 3, characterized in that: The specific method for judging high-frequency and low-frequency components is as follows: The unprocessed deformation monitoring data is recorded as the original monitoring data, and the original monitoring data is decomposed by the local mean decomposition method to obtain several signal components represented by high-frequency product functions. The signal components in the time domain are converted to the frequency domain by Fourier transform, and the frequency w and the power spectrum density function P(w) of the signal component are calculated to determine the low-frequency band range, and the power spectrum data is extracted within the determined low-frequency band range. The linear relationship between the power spectrum density and the frequency within the low-frequency band range is determined by the formula logP(w)=αlogw+β+o(w), wherein α represents the slope of the linear relationship, β represents the offset constant of the power spectrum density changing with frequency, and in this embodiment, β is set to 1, o(w) represents the fitting function, and the objective function of the fitting function is set. The true value of α is determined by the objective function of the fitting function, and then the low-high frequency boundary value is set. when When the signal is high frequency, it is judged that the signal is low frequency.

5. A method for processing deformation monitoring data of multiple base stations according to claim 4, characterized in that: Set the objective function of the fitting function. The specific method is: By formula represents the objective function of the fitting function, where a and b represent the lower limit and upper limit of the low frequency band respectively, and the fitting function that satisfies the objective function is determined, and the slope α that satisfies the linear relationship between the power spectrum density and the frequency is further obtained.

6. A method for processing deformation monitoring data of multiple base stations according to claim 1, characterized in that: Determine the standard state data corresponding to different types of deformation data. The specific method is: The deformation monitoring object is simulated, and a physical model with the structure of the deformation monitoring object is constructed. The numerical model of the deformation monitoring object is established by using finite element analysis and discrete element analysis methods. The numerical model is solved according to the actual material properties and geometric dimensions of the deformation monitoring object to obtain the initial simulation object model. The simulation environment parameters are set according to the real environment, and the simulation experiment is carried out with a period of time t. Various deformation data are used as input of the simulation model. The various deformation data include: the displacement change rate, the tilt change rate, the vibration amplitude change rate and the structural damage area change rate within the period of time t. According to the input of the simulation model, n cycles are simulated, and the deformation degree of the deformation monitoring object in n cycles is output. Based on the deformation degree, it is judged whether the input of the simulation model affects the normal deformation state, and the deformation data range in which the abnormal deformation state will not occur is determined. The maximum value boundary of each type of deformation data range is taken as the standard state data of the corresponding type.

7. A method for processing deformation monitoring data of multiple base stations according to claim 1, characterized in that: The specific method for judging the deformation state of the deformation monitoring object within a time period is as follows: Combining the deformation data and standard state data in the current time period, the change ratio of each type of deformation data is obtained, which are recorded as D, V, Q and S respectively. The deformation trend evaluation value is obtained, where H represents the structural strength of the deformation monitoring object before the current cycle, U represents the material strength of the deformation monitoring object, k1, k2, k3, and k4 are weight coefficients, and k1+k2+k3+k4=1. The deformation trend evaluation value of each base station is integrated, and the average value Fa′ is taken to determine the deformation trend evaluation threshold I, where I is the maximum deformation trend evaluation value that keeps the deformation monitoring object in a normal deformation state. When Fa′ is less than or equal to I, it is judged that the deformation monitoring object is in a normal deformation state within n cycles, that is, the deformation trend within n cycles is within a controllable range. Otherwise, it is judged that the deformation monitoring object is in an abnormal deformation state within n cycles, that is, the deformation trend within n cycles is not within a controllable range.

8. A method for processing deformation monitoring data of multiple base stations according to claim 7, characterized in that: Combining the deformation data and standard state data in the current time period, the change ratio of each type of deformation data is obtained. The specific steps are: Obtain the displacement change rate d of each base station in the current time period, compare it with the standard state data of the displacement change rate, and obtain the displacement change ratio D; Obtain the tilt change rate v in the current time period, compare it with the standard state data of the tilt change rate, and obtain the tilt change ratio V; Obtain the vibration amplitude change rate q within the current time period, compare it with the standard state data of the vibration amplitude change rate, and obtain the vibration amplitude change ratio Q; The structural damage area change rate s in the current time period is obtained, and compared with the standard state data of the structural damage area change rate, the structural damage area change ratio S is obtained.

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