Deformation monitoring data processing method based on GNSS technology

Abstract

The invention discloses a deformation monitoring data processing method based on a GNSS technology, relates to the technical field of monitoring data, and effectively improves the precision and reliability of deformation monitoring data through multiple times of data acquisition, accurate analysis and comprehensive integration. According to the method, a multi-device collaborative data processing flow is adopted, and technical means such as turning point identification, basic variable value calculation, segmentation analysis and characteristic value optimization are combined, so that the defects of data errors and device deviation in a traditional method are overcome, and visual display of a data trend and accurate extraction of standard data are also realized; on the whole, the method is comprehensively optimized in links of data acquisition, analysis, integration and the like, adapts to various complex application scenes, and provides an efficient and accurate solution for deformation monitoring; the method has the advantages that the feasibility and flexibility of operation are considered while the monitoring precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring data, and particularly to a method for processing deformation monitoring data based on GNSS technology. Background Technique

[0002] With the continuous expansion of the scale of modern engineering construction, the importance of structural safety and stability monitoring has become increasingly prominent. Especially in the fields of large-scale infrastructure, geological disaster monitoring, and mine development, the demand for deformation monitoring is becoming more urgent. The accuracy and real-time performance of deformation monitoring are directly related to the safety assessment of engineering and the effect of disaster warning. At present, GNSS (Global Navigation Satellite System) technology has become one of the key means of deformation monitoring due to its high precision and all-weather monitoring ability.

[0003] However, in practical applications, traditional GNSS data processing methods face multiple challenges. First, due to the susceptibility of measurement devices to environmental interference under different acquisition conditions, there are often certain errors in the data collected in a single acquisition; second, there are often deviations in the data between different devices, making it difficult to directly integrate; third, traditional methods for data processing and analysis mostly use simple statistical means, making it difficult to comprehensively extract the characteristics of the data, resulting in limitations in the accuracy of monitoring results. Therefore, there is an urgent need for a method for processing deformation monitoring data that can integrate multi-device data, improve monitoring accuracy, and adapt to complex scenarios to meet the needs of actual engineering and scientific research. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method for processing deformation monitoring data based on GNSS technology, which solves the problems in the background technique.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for processing deformation monitoring data based on GNSS technology, including:

[0006] Step 1: Based on GNSS technology, obtain the deformation monitoring data collected by the measurement device within the relevant period, display all the data of the deformation monitoring data within the relevant period by establishing a coordinate system, and determine the relevant base change value of the deformation monitoring data within the relevant period;

[0007] Step 2: According to the relevant base change value of the deformation monitoring data, perform segmented analysis on the deformation monitoring data, select the optimal analysis segment from the several separated data segments, and determine the standard deformation monitoring data from the optimal analysis segment.

[0008] As a further aspect of the present invention: In the above Step 1, the specific method for determining the relevant base change value of the deformation monitoring data within the relevant period is:

[0009] AS1: Obtain the relevant period T set by the measuring device, where T is a preset value, specifically set by professional staff;

[0010] AS2: Confirm all the deformation monitoring data monitored within the relevant period T one by one. Then, according to the different deformation monitoring data corresponding to different moments within the relevant period T, establish a coordinate system, and generate a deformation monitoring data change curve of the relevant period T in the first quadrant of the coordinate system. Since the deformation monitoring data collected at different moments is different, determine the relevant points in the first quadrant of the coordinate system, and then connect the relevant points with line segments to obtain the deformation monitoring data change curve of the relevant period T; among them, the horizontal coordinate axis is the time line, and its vertical coordinate axis is the specific deformation monitoring data;

[0011] AS3: Identify and determine the turning points existing in the deformation monitoring data change curve. According to the confirmed turning points, label the deformation monitoring data corresponding to each turning point as Xi, where i represents different turning points, and obtain the maximum value X max and the minimum value X min ;

[0012] AS4: Determine the relevant base change value G of the deformation monitoring data through the formula , where h1 is a preset value.

[0013] As a further solution of the present invention: in the second step, the specific method of segmenting and analyzing the deformation monitoring data according to the relevant base change value of the deformation monitoring data, selecting the optimal analysis segment from the several separated data segments, and determining the standard deformation monitoring data from the optimal analysis segment is as follows:

[0014] BS1: Sort all the deformation monitoring data within the relevant period T according to the time before and after the collection of the corresponding deformation monitoring data to determine the data sequence;

[0015] BS2: Identify whether the change difference between adjacent data in the data sequence is of the same type. According to the judgment of the same type of value, determine several groups of data segments.

[0016] As a further solution of the present invention: it further includes:

[0017] BS3: From the confirmed data segments, determine the time difference RF i between the corresponding initial data and the end data of the data segment, where i represents different data segments, and then confirm the total sum value of the change differences between different deformation monitoring data within the corresponding data segment, and label it as FZ i , and through the formula Tz i =(RF i ×g1)÷(FZ i×g2) Determine the characteristic value Tz i , where g1 and g2 are both preset influencing factors;

[0018] BS4: Confirm the characteristic values ​​Tz of different data segments in turn i , from different Tz i The data segment corresponding to the maximum value is selected, and this data segment is marked as the optimal analysis segment. Several groups of deformation monitoring data corresponding to the optimal analysis segment are averaged to determine the standard deformation monitoring data.

[0019] As a further solution of the present invention: in the BS2, whether the change difference between adjacent data in its data sequence is of the same type is identified, and according to the judgment of the same type, a specific method of determining a plurality of groups of data segments is as follows:

[0020] Identify the change difference between the first deformation monitoring data and the second deformation monitoring data, where the change difference = |previous deformation monitoring data-next deformation monitoring data|. If the change difference ≤ G, calibrate the two deformation monitoring data as the same type of data, and then identify whether the second deformation monitoring data and the third deformation monitoring data are the same type of data, and so on, until the confirmed subsequent deformation monitoring data is not the same type of data, and stop, and divide the several deformation monitoring data before this deformation monitoring data into the first data segment; and then start from this deformation monitoring data, confirm the same type of values ​​in the future, and process step by step, and confirm the subsequent related several groups of data segments one by one.

[0021] As a further solution of the present invention: after step 2, the method further comprises:

[0022] Step 3: According to the same processing method in step 1 and step 2, the standard deformation monitoring data of the deformation monitoring data collected by different measuring devices in the relevant period are confirmed one by one to obtain the final accurate deformation monitoring data.

[0023] The present invention provides a deformation monitoring data processing method based on GNSS technology. Compared with the prior art, it has the following beneficial effects:

[0024] The present invention effectively improves the accuracy and reliability of deformation monitoring data through multiple data collection, precise analysis and comprehensive integration. The method adopts a data processing flow of multi-device collaboration, combined with technical means such as turning point identification, basis value calculation, segmented analysis and eigenvalue optimization, which not only overcomes the shortcomings of data errors and equipment deviations in traditional methods, but also realizes the intuitive display of data trends and the accurate extraction of standard data.

[0025] Through the analysis of data segmentation and the calculation of eigenvalues, the present invention optimizes the processing method of deformation monitoring data, can effectively distinguish similar data and screen out the optimal data segments, which greatly reduces the errors caused by noise and random fluctuations in the original data; by using the sum of the time difference and the change difference between the initial value and the end value in the data sequence, the eigenvalue is further calculated to ensure that the selected optimal data segment has the highest reliability; through the mean processing of the optimal data segment, the standard deformation monitoring data is finally determined, overcoming the limitations of relying on simple up and down fluctuation values in the traditional method, and significantly improving the accuracy and credibility of the data analysis results.

[0026] Overall, this method is comprehensively optimized in the links of data collection, analysis and integration, adapts to a variety of complex application scenarios, and provides an efficient and accurate solution for deformation monitoring. Its advantage lies in improving the monitoring accuracy while taking into account the feasibility and flexibility of operation. Brief Description of the Drawings

[0027] The present invention will be further described below with reference to the accompanying drawings.

[0028] Figure 1 It is a flowchart of the steps of a method for processing deformation monitoring data based on GNSS technology of the present invention. Detailed Embodiments

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] Embodiment 1

[0031] Please refer to Figure 1 , the present invention provides a method for processing deformation monitoring data based on GNSS technology, including;

[0032] Step 1: Based on GNSS technology, obtain the deformation monitoring data collected by the measuring device in the relevant period, display all the data of the deformation monitoring data in the relevant period by establishing a coordinate system, and determine the relevant basic change value of the deformation monitoring data in the relevant period;

[0033] It should be noted that in the GNSS technology, when collecting deformation monitoring data for different measurement devices, within a preset relevant period, a certain item of data needs to be collected multiple times, and based on the multiple deformation monitoring data of this item collected, analysis is carried out to determine the most accurate collected data; the measurement devices include GNSS receivers, reference stations, differential GNSS systems, and inertial measurement units (IMUs), etc., and the deformation monitoring data includes position data, displacement data, tilt and attitude angle data, etc. For position data, there may be error situations through a single collection, so within the preset relevant period, the position data needs to be collected multiple times;

[0034] The specific method for determining the relevant base change value of the deformation monitoring data within its relevant period is as follows:

[0035] AS1: Obtain the relevant period T set by the measurement device, where T is a preset value, specifically set by professional staff;

[0036] AS2: Confirm all the deformation monitoring data monitored within the relevant period T one by one. Then, according to the different deformation monitoring data corresponding to different times within the relevant period T, establish a coordinate system, generate a deformation monitoring data change curve of the relevant period T in the first quadrant of the coordinate system. Since the deformation monitoring data collected at different times is different, determine the relevant points in the first quadrant of the coordinate system, and then connect the relevant points with line segments to obtain the deformation monitoring data change curve of the relevant period T; among them, the horizontal coordinate axis is the time line, and its vertical coordinate axis is the specific deformation monitoring data;

[0037] AS3: Identify and determine the turning points existing in the deformation monitoring data change curve. According to the confirmed turning points, label the deformation monitoring data corresponding to each turning point as Xi, where i represents different turning points, and obtain the maximum value X max and the minimum value X min ;

[0038] AS4: Determine the relevant base change value G of the deformation monitoring data through the formula where h1 is a preset value, and its specific value is determined by the operator according to experience. In this embodiment, h1 = 2;

[0039] Exemplarily, the turning point is further explained as: If there are three points, namely A, B, and C, where the deformation monitoring data corresponding to A is 0.5, B is 0.7, and C is 0.6, then the change trend between A - B is upward, and the change trend between B - C is downward, so point B is the turning point;

[0040] By collecting and analyzing data multiple times within a preset period based on GNSS technology, the accuracy of data collection is significantly improved. In particular, when processing location data, which may contain errors due to a single collection, the reliability of the data is ensured by repeated collection and analysis of multiple sets of data. In addition, by establishing a coordinate system to visualize the deformation monitoring data, it is not only possible to intuitively display the trend of data changes over time, but also to identify key turning points and data change patterns, providing a reliable basis for subsequent data analysis. The identification of turning points and the calculation of base change values ​​further optimize the basic quality of the data and effectively improve the overall accuracy and efficiency of deformation monitoring.

[0041] Step 2: According to the relevant base deformation values ​​of the deformation monitoring data, the deformation monitoring data is segmented and analyzed, and the optimal analysis segment is selected from the separated data segments, and the standard deformation monitoring data is determined from the optimal analysis segment;

[0042] It should be noted that the standard deformation monitoring data is the data with the most accurate value in the process of collecting deformation monitoring data. In the original data confirmation process, generally through the set monitoring program, when a certain value fluctuates up and down for a few seconds, such value is used as the data of this collection of deformation monitoring data. However, this original value confirmation method still has some errors in accuracy, so it is necessary to determine the deformation monitoring data with the highest calibration degree;

[0043] The specific method of performing segmented analysis on the deformation monitoring data according to the relevant base deformation values ​​of the deformation monitoring data, selecting the optimal analysis segment from the separated data segments, and determining the standard deformation monitoring data from the optimal analysis segment is as follows:

[0044] BS1: All deformation monitoring data within the relevant period T are sorted according to the time when the corresponding deformation monitoring data were collected to determine the data sequence;

[0045] BS2: Identify whether the change difference between adjacent data in the data sequence is of the same type, identify the change difference between the first deformation monitoring data and the second deformation monitoring data, the change difference = |previous deformation monitoring data - next deformation monitoring data|, if the change difference ≤ G, then mark the two deformation monitoring data as the same type of data, then identify whether the second deformation monitoring data and the third deformation monitoring data are of the same type, and so on, until the confirmed subsequent deformation monitoring data is not of the same type of data, and then divide the several deformation monitoring data before this deformation monitoring data into the first data segment; then start from this deformation monitoring data, confirm the same type of values, and process step by step, and confirm the subsequent related several groups of data segments one by one;

[0046] Exemplarily, assume that the collected deformation monitoring data sequence is {20, 19, 20, 21, 23, 29, 23, 22, 24, 23}. Then the calibrated relevant base change value G = 5. The differences between the data such as 20, 19, 20, 21, 23 are less than 5, so they belong to the first data segment. The data 29 does not conform. The subsequent second data segment is 23, 22, 24, 23;

[0047] BS3: From the confirmed data segments, determine the time difference RF between the corresponding times of the initial data and the end data of the data segments i , where i represents different data segments. Then, confirm the total value of the change differences between different deformation monitoring data within the corresponding data segments, and calibrate it as FZ i , through the formula Tz i =(RF i ×g1)÷(FZ i ×g2) to determine the characteristic value Tz i , where g1 and g2 are both preset influence factors, and their specific values are determined by the operator according to experience. The smaller the value of FZ i , the greater the time difference, and the greater the generated characteristic value;

[0048] BS4: Sequentially confirm the characteristic values Tz of different data segments i , select the data segment corresponding to the maximum value from different Tz i , calibrate this data segment as the optimal analysis segment, and perform mean processing on several groups of deformation monitoring data corresponding to the optimal analysis segment to determine the standard deformation monitoring data;

[0049] This step optimizes the processing method of deformation monitoring data through the analysis of data segments and the calculation of characteristic values, can effectively distinguish similar data and screen out the optimal data segment. This method greatly reduces the errors caused by noise and random fluctuations in the original data; uses the time difference between the initial value and the end value in the data sequence and the total change difference to further calculate the characteristic value to ensure that the selected optimal data segment has the highest reliability; finally determines the standard deformation monitoring data through the mean processing of the optimal data segment, overcomes the limitations of relying on simple up and down fluctuation values in the traditional method, and significantly improves the accuracy and credibility of the data analysis results.

[0050] Example 2

[0051] In the specific implementation process of this example, on the basis of Example 1, and the difference from Example 1 is that this example further includes Step 3:

[0052] Step 3: According to the same processing methods in Step 1 and Step 2, confirm one by one the standard deformation monitoring data of the deformation monitoring data collected by different measuring devices during the relevant period to obtain the final accurate deformation monitoring data;

[0053] By separately processing and comprehensively analyzing the deformation monitoring data collected by different measuring devices, this step effectively eliminates the possible deviations between devices, ensuring the consistency and accuracy of cross-device data. The integrated deformation monitoring data can not only reflect the common characteristics of the data of each device, but also further extract the most accurate and reliable monitoring results on this basis.

[0054] Embodiment 3

[0055] In the specific implementation process of this embodiment, it includes all the implementation processes of the above two groups of embodiments.

[0056] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0057] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A deformation monitoring data processing method based on GNSS technology, characterized in that: include: Step 1: Based on GNSS technology, the deformation monitoring data collected by the measuring equipment in the relevant period is obtained, all the data of the deformation monitoring data in the relevant period is displayed by establishing a coordinate system, and the relevant base change value of the deformation monitoring data in the relevant period is determined; Step 2: According to the relevant base deformation values ​​of the deformation monitoring data, the deformation monitoring data is segmented and analyzed, and the optimal analysis segment is selected from the separated data segments, and the standard deformation monitoring data is determined from the optimal analysis segment.

2. The deformation monitoring data processing method based on GNSS technology according to claim 1 is characterized in that: In the step 1, the specific method of determining the relevant base change value of the deformation monitoring data within the relevant period is: AS1: Obtain the relevant period T set by the measuring equipment, where T is a preset value, which is specifically set by professional staff; AS2: Confirm all deformation monitoring data monitored within the relevant period T one by one, then establish a coordinate system according to the different deformation monitoring data corresponding to different times within the relevant period T, and generate a deformation monitoring data change curve of the relevant period T in the first quadrant of the coordinate system. Since the deformation monitoring data collected at different times are different, determine the relevant points in the first quadrant of the coordinate system, and then connect the relevant points with line segments to obtain the deformation monitoring data change curve of the relevant period T; wherein, the horizontal coordinate axis is the timeline, and the vertical coordinate axis is the specific deformation monitoring data; AS3: Identify and determine the turning points in the deformation monitoring data change curve. According to the confirmed turning points, calibrate the deformation monitoring data corresponding to each turning point as Xi, where i represents different turning points, and obtain the maximum value X from the deformation monitoring data Xi. max and the minimum value X min ; AS4: By formula Determine the relevant base change value G of the deformation monitoring data, where h1 is a preset value.

3. The deformation monitoring data processing method based on GNSS technology according to claim 2 is characterized in that: In the step 2, the deformation monitoring data is segmented and analyzed according to the relevant base deformation values ​​of the deformation monitoring data, an optimal analysis segment is selected from the separated data segments, and the specific method of determining the standard deformation monitoring data from the optimal analysis segment is as follows: BS1: All deformation monitoring data within the relevant period T are sorted according to the time when the corresponding deformation monitoring data were collected to determine the data sequence; BS2: Identify whether the change difference between adjacent data in its data sequence is of the same type, and determine several groups of data segments based on the judgment of the same type of values.

4. The deformation monitoring data processing method based on GNSS technology according to claim 3 is characterized in that: Also includes: BS3: From the confirmed data segment, determine the time difference RF between the initial data and the corresponding time of the end data of the corresponding data segment i , where i represents different data segments, and then confirm the sum of the change differences between different deformation monitoring data in the corresponding data segment and calibrate it as FZ i , through the formula Tz i =(RF i ×g1)÷(FZ i ×g2) Determine the characteristic value Tz i , where g1 and g2 are both preset influencing factors; BS4: Confirm the characteristic values ​​Tz of different data segments in turn i , from different Tz i The data segment corresponding to the maximum value is selected, and this data segment is marked as the optimal analysis segment. Several groups of deformation monitoring data corresponding to the optimal analysis segment are averaged to determine the standard deformation monitoring data.

5. The deformation monitoring data processing method based on GNSS technology according to claim 3 is characterized in that: In the BS2, whether the change difference between adjacent data in the data sequence is of the same type is identified, and the specific method of determining several groups of data segments according to the judgment of the same type is as follows: Identify the change difference between the first deformation monitoring data and the second deformation monitoring data, where the change difference = |previous deformation monitoring data-next deformation monitoring data|. If the change difference ≤ G, calibrate the two deformation monitoring data as the same type of data, and then identify whether the second deformation monitoring data and the third deformation monitoring data are the same type of data, and so on, until the confirmed subsequent deformation monitoring data is not the same type of data, and stop, and divide the several deformation monitoring data before this deformation monitoring data into the first data segment; and then start from this deformation monitoring data, confirm the same type of values ​​in the future, and process step by step, and confirm the subsequent related several groups of data segments one by one.

6. The deformation monitoring data processing method based on GNSS technology according to claim 1 is characterized in that: The step 2 further includes: Step 3: According to the same processing method in step 1 and step 2, the standard deformation monitoring data of the deformation monitoring data collected by different measuring devices in the relevant period are confirmed one by one to obtain the final accurate deformation monitoring data.

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