A deformation monitoring data analysis method and system

Abstract

The application discloses a kind of deformation monitoring data analysis method and system, belong to intelligent analysis technical field.The method includes: obtaining the monitoring data sequence of each deformation monitoring point in target engineering area in continuous multiple observation periods;Calculate the horizontal variation and vertical variation between adjacent observation periods;Identify suspected re-burial observation period of measuring point;Effectiveness verification is carried out to suspected re-burial observation period of measuring point, exclude false mutation caused by non-monitoring point replacement factor, and the final suspected re-burial observation period of measuring point is used as actual re-burial observation period of corresponding monitoring point;According to actual re-burial observation period, the cumulative variation of each monitoring point in subsequent observation period is calculated.The application realizes the automatic identification of measuring point re-burial event and the seamless connection of cumulative deformation, guarantees the continuity and physical authenticity of deformation process curve, significantly improves the intelligent level and accuracy of deformation monitoring data processing.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, specifically to a deformation monitoring data analysis method and system. Background Technology

[0002] In fields such as building construction, tunnel engineering, foundation pit engineering, slope engineering, and water conservancy and hydropower engineering, deformation monitoring of buildings, structures, and their surrounding environment is a key means to ensure construction safety and operational stability. Deformation monitoring typically includes horizontal displacement monitoring and settlement monitoring. On-site measurement work generates massive amounts of monitoring data. Traditional processing methods usually rely on manual recording, manual calculation, or the use of scattered single-point tools. With the extension of the monitoring cycle and the increase in the number of monitoring points, this traditional approach has exposed a series of problems, such as low data management efficiency: monitoring data is mostly stored in scattered text files or spreadsheets, lacking a unified management platform, easily leading to data loss, version confusion, and difficulty in tracing historical data and comparing data from multiple periods; cumbersome and error-prone analysis and calculation work: after each new data entry, the change, cumulative change, and rate of change for each monitoring point need to be calculated manually or semi-manually; and time-consuming report generation: compiling the analysis results into a standardized report requires a large amount of repetitive copying, pasting, and formatting work.

[0003] To address the aforementioned issues, various deformation monitoring data management systems and methods have been proposed in the prior art. For example, Chinese Patent Application No. CN115478567A discloses a monitoring method for open-cut tunnel construction that passes over underground railway tracks and is adjacent to surrounding buildings. This method solves the technical problem of ensuring monitoring accuracy and reliability in open-cut tunnel construction that passes over underground railway tracks and is adjacent to surrounding buildings. This invention includes foundation pit monitoring and surrounding building monitoring. The foundation pit monitoring includes water level monitoring, horizontal and vertical displacement monitoring of the slope top, surface settlement monitoring outside the pit, bottom heave monitoring, soil tilt monitoring, retaining wall inclination monitoring, anchor bolt internal force monitoring, column pile vertical displacement monitoring, concrete support axial force monitoring, and steel support axial force monitoring. The surrounding building monitoring includes vertical displacement monitoring and horizontal displacement monitoring of surrounding buildings. The monitoring methods for vertical and horizontal displacement monitoring of surrounding buildings are the same as those for foundation pit monitoring.

[0004] However, in actual engineering projects, there is a lack of accurate and automated solutions for calculating the cumulative changes when monitoring points are destroyed and then re-buried. It is common for monitoring points to be damaged due to construction machinery, foundation pit collapse, human-caused damage, etc. When a monitoring point is destroyed, a new monitoring point needs to be re-buried in the original location or nearby to continue observation. However, the current process of re-burying monitoring points relies solely on manual reporting and lacks effective means of information verification. Summary of the Invention

[0005] In order to at least overcome the above-mentioned shortcomings in the prior art, the purpose of this application is to provide a deformation monitoring data analysis method and system.

[0006] In a first aspect, embodiments of this application provide a deformation monitoring data analysis method, including: Acquire monitoring data sequences for each deformation monitoring point within the target engineering area over multiple consecutive observation periods; the monitoring data sequences include horizontal displacement data and vertical displacement data; For each monitoring point, calculate the horizontal and vertical changes between adjacent observation periods; Multidimensional joint mutation detection is performed based on the horizontal and vertical changes to identify the observation period for suspected re-embedding of monitoring points; the observation period for suspected re-embedding of monitoring points is the observation period during which the corresponding monitoring point has the risk of re-embedding. The validity of the observation period for the re-embedding of the suspected measuring points was verified to exclude false abrupt changes caused by factors other than the replacement of the monitoring points, and the final observation period for the re-embedding of the suspected measuring points was taken as the actual observation period for the re-embedding of the corresponding monitoring points. The cumulative change of each monitoring point in the subsequent observation period is calculated based on the actual re-burying observation period.

[0007] In one possible implementation, the observation period for identifying suspected measuring points and re-burying them includes: When both the horizontal and vertical changes at the monitoring point are greater than the corresponding preset abrupt change thresholds, the observation period corresponding to the horizontal and vertical changes is determined to be the observation period for the suspected monitoring point to be re-buried.

[0008] In one possible implementation, the horizontal change includes a first horizontal change and a second horizontal change orthogonal to the first horizontal change. The observation period for identifying suspected measuring points and re-burying them also includes: The standard deviations of the first horizontal change, the second horizontal change, and the vertical change were obtained from multiple consecutive observation periods prior to the observation period and used as reference standard deviations for the changes in different directions. The reference standard deviation is multiplied by a factor to form the mutation determination threshold.

[0009] In one possible implementation, validating the observation period for the re-embedded monitoring points includes: When at least two monitoring points within the target engineering area are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a global event. When there are no at least two monitoring points in the target project area that are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a local event; The observation period for re-burying suspected monitoring points corresponding to local events is taken as the actual re-burying observation period; the observation period for re-burying suspected monitoring points corresponding to global events is taken as a false mutation caused by factors other than monitoring point replacement.

[0010] In one possible implementation, calculating the cumulative change of each monitoring point in subsequent observation periods based on the actual re-burying observation period includes: When the current observation period is later than the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period is calculated, plus the difference between the current monitoring value and the first monitoring value after the actual re-burying observation period, as the cumulative change of the current observation period. When the current observation period is equal to the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period shall be used as the cumulative change of the current observation period.

[0011] Secondly, this application also provides a deformation monitoring data analysis system, including: The acquisition unit is configured to acquire a sequence of monitoring data for each deformation monitoring point within the target engineering area over multiple consecutive observation periods; the monitoring data sequence includes horizontal displacement data and vertical displacement data. The calculation unit is configured to calculate the horizontal and vertical changes between adjacent observation periods for each monitoring point; The detection unit is configured to perform multi-dimensional joint mutation detection based on the horizontal and vertical changes to identify the observation period for suspected re-embedding of monitoring points; the observation period for suspected re-embedding of monitoring points is the observation period during which the corresponding monitoring point has a risk of re-embedding. The verification unit is configured to verify the validity of the re-embedding observation period of the suspected measuring point, exclude false changes caused by factors other than the replacement of the monitoring point, and take the final re-embedding observation period of the suspected measuring point as the actual re-embedding observation period of the corresponding monitoring point. The cumulative unit is configured to calculate the cumulative change of each monitoring point in subsequent observation periods based on the actual re-burying observation period.

[0012] In one possible implementation, the detection unit is further configured as follows: When both the horizontal and vertical changes at the monitoring point are greater than the corresponding preset abrupt change thresholds, the observation period corresponding to the horizontal and vertical changes is determined to be the observation period for the suspected monitoring point to be re-buried.

[0013] In one possible implementation, the horizontal change includes a first horizontal change and a second horizontal change orthogonal to the first horizontal change. The detection unit is further configured to: The standard deviations of the first horizontal change, the second horizontal change, and the vertical change were obtained from multiple consecutive observation periods prior to the observation period and used as reference standard deviations for the changes in different directions. The reference standard deviation is multiplied by a factor to form the mutation determination threshold.

[0014] In one possible implementation, the verification unit is further configured as follows: When at least two monitoring points within the target engineering area are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a global event. When there are no at least two monitoring points in the target project area that are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a local event; The observation period for re-burying suspected monitoring points corresponding to local events is taken as the actual re-burying observation period; the observation period for re-burying suspected monitoring points corresponding to global events is taken as a false mutation caused by factors other than monitoring point replacement.

[0015] In one possible implementation, the accumulation unit is further configured as follows: When the current observation period is later than the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period is calculated, plus the difference between the current monitoring value and the first monitoring value after the actual re-burying observation period, as the cumulative change of the current observation period. When the current observation period is equal to the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period shall be used as the cumulative change of the current observation period.

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: This invention provides a deformation monitoring data analysis method and system. It introduces an automatic identification mechanism for the re-burying observation period based on multi-dimensional joint mutation detection, effectively solving the long-standing technical problem in the field of deformation monitoring where errors in the calculation of cumulative changes are caused by human error in reporting monitoring point damage and re-burying information. At the same time, through dynamic thresholds and dual verification mechanisms, combined with the comparison of the consistency of surrounding measuring points to exclude global events such as overall changes in the benchmark point, it ensures high accuracy and low false alarm rate in the identification of the re-burying observation period. In addition, with the connection algorithm of inheriting the data of the re-burying period and subsequent accurate accumulation, it realizes a seamless and smooth transition of the cumulative deformation between the old and new physical measuring points, fundamentally ensuring the continuity and physical authenticity of the deformation process curve. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the method steps in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0019] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] Please refer to the following: Figure 1 The above is a flowchart illustrating a deformation monitoring data analysis method provided in an embodiment of the present invention. Further, the deformation monitoring data analysis method may specifically include the contents described in steps S1-S5.

[0021] S1: Obtain the monitoring data sequence of each deformation monitoring point within the target engineering area over multiple consecutive observation periods; the monitoring data sequence includes horizontal displacement data and vertical displacement data; S2: For each monitoring point, calculate the horizontal and vertical changes between adjacent observation periods; S3: Perform multi-dimensional joint mutation detection based on the horizontal and vertical changes to identify the observation period for suspected re-embedding of monitoring points; the observation period for suspected re-embedding of monitoring points is the observation period during which the corresponding monitoring point has the risk of re-embedding. S4: Verify the validity of the observation period for the re-embedding of the suspected measuring points, exclude false changes caused by factors other than the replacement of the monitoring points, and take the final observation period for the re-embedding of the suspected measuring points as the actual observation period for the re-embedding of the corresponding monitoring points. S5: Calculate the cumulative change of each monitoring point in the subsequent observation period based on the actual re-burying observation period.

[0022] In the implementation of this application embodiment, the data obtained comes from multiple dimensions. It does not only obtain monitoring data in a single dimension, but simultaneously obtains three-dimensional coordinate data composed of two-dimensional horizontal direction and one-dimensional vertical direction. When a monitoring point is destroyed and re-buried, the absolute position of the newly buried measuring point in space will definitely be different from that of the old measuring point. Therefore, this change of physical carrier will leave observable data traces in three dimensions at the same time. However, relying solely on a single dimension for abrupt change detection cannot distinguish between the change of measuring point and the actual acceleration of deformation in that direction. The three-dimensional synchronous detection utilizes the physical correlation between the three.

[0023] In this embodiment, the three-dimensional coordinate time series of each monitoring point is transformed into a three-dimensional change time series. This allows information originally scattered in absolute coordinates to be concentrated in the jump amplitude between adjacent periods. Under normal working conditions, the three-dimensional coordinates of the monitoring point change slowly with engineering deformation, and the change between adjacent periods is usually small and continuous. However, when the monitoring point is re-buried, the absolute coordinates of the new monitoring point are significantly different from those of the old monitoring point, resulting in an abnormally large change in the current period that far exceeds the normal deformation rate. Therefore, the essence of change calculation is to perform a first-order difference transformation on the original monitoring data to map the discrete event of monitoring point replacement into a quantifiable abnormal peak signal in the time series, thereby laying a calculable foundation for subsequent mutation detection.

[0024] In this embodiment, multi-dimensional joint abrupt change detection is performed based on the horizontal and vertical changes to identify suspected re-embedding observation periods for monitoring points. Only when the absolute values ​​of the horizontal changes ΔX, ΔY, and vertical changes ΔH of a monitoring point in a certain observation period simultaneously exceed their respective abrupt change thresholds is that period identified as a suspected re-embedding observation period. In deformation monitoring, unidirectional data abrupt changes may have multiple causes. A single abrupt change in ΔX or ΔY may be due to actual accelerated deformation or local coordinate system rotation of the monitoring point in the X or Y direction; while a single abrupt change in ΔH may be due to… The cause could be sudden settlement of the foundation or leveling measurement error; only when ΔX, ΔY, and ΔH jump simultaneously can there be a unique explanation in physics, indicating a change in the absolute spatial position of the monitoring point. This overall change can only be explained in one way on the engineering site: the physical carrier of the monitoring point has changed. Therefore, this embodiment achieves highly specific identification of monitoring point replacement events through three-dimensional joint detection. It will not misreport normal deformation as re-burying, nor will it misjudge observation errors in a single direction as monitoring point replacement, thus achieving a good balance between identification sensitivity and false alarm rate.

[0025] In this embodiment, to verify the effectiveness of the observation period for suspected re-embedding of monitoring points and exclude false mutations caused by factors other than monitoring point replacement, the following verification methods can be used to exclude false mutations: Extract the change data of several observation periods before and after the suspected period, and check whether the mutation is an isolated event. Re-embedding of monitoring points is a one-time physical operation, which is isolated and transient in the time dimension. It only causes coordinate jumps in that observation period, and subsequent observation periods will return to normal changes on the new benchmark. However, continuous environmental changes or systematic instrument drift will manifest as continuous anomalies in the time dimension, that is, data deviating from the normal range for multiple consecutive periods. Therefore, if the changes before and after the suspected period have returned to normal, the judgment of monitoring point replacement is supported; if the anomalies continue for multiple consecutive periods after the suspected period, it is excluded; or check whether other monitoring points in the same engineering area have experienced similar mutations during the same period. Re-embedding of monitoring points is usually caused by local factors, which are independent and local in the spatial dimension, affecting only individual monitoring points, while adjacent monitoring points are not affected. Systematic errors such as changes in the overall benchmark point or improper instrument alignment can affect multiple or even all monitoring points simultaneously. Therefore, if only the current monitoring point experiences a sudden change while surrounding monitoring points remain normal, the decision to replace the independent monitoring point is supported; if multiple monitoring points experience simultaneous and synchronous changes, then the decision is ruled out.

[0026] In one possible implementation, the observation period for identifying suspected measuring points and re-burying them includes: When both the horizontal and vertical changes at the monitoring point are greater than the corresponding preset abrupt change thresholds, the observation period corresponding to the horizontal and vertical changes is determined to be the observation period for the suspected monitoring point to be re-buried.

[0027] In the implementation of this application embodiment, conventional anomaly detection in the prior art pursues high recall and allows for a certain degree of false alarms. However, the identification in this application embodiment pursues high accuracy because the cost of false alarms is extremely high. Once a non-measuring point replacement is mistakenly identified as a measuring point replacement, it will lead to incorrect connection calculations and contaminate all subsequent cumulative change data. Therefore, this application embodiment forms strong constraints in three dimensions, making the threshold for triggering identification extremely high. Only when the observation period of synchronous jump in three directions is met simultaneously can it enter the subsequent verification stage, thereby controlling the false alarm rate from the source. When the physical carrier of the monitoring point is destroyed and reburied, the newly buried measuring point is a completely new physical entity. Its absolute position in three-dimensional space will definitely be significantly different from that of the old measuring point. This difference is synchronous in three dimensions, not just in one direction. It will show an overall jump in three-dimensional coordinate space. Normal stratum deformation cannot cause synchronous jumps of several centimeters or even tens of centimeters in three dimensions at the same time. A single measurement error cannot simultaneously produce large deviations in three directions. Therefore, by jointly detecting the horizontal and vertical changes, high specificity identification of the specific physical event of measuring point replacement is achieved.

[0028] In one possible implementation, the horizontal change includes a first horizontal change and a second horizontal change orthogonal to the first horizontal change. The observation period for identifying suspected measuring points and re-burying them also includes: The standard deviations of the first horizontal change, the second horizontal change, and the vertical change were obtained from multiple consecutive observation periods prior to the observation period and used as reference standard deviations for the changes in different directions. The reference standard deviation is multiplied by a factor to form the mutation determination threshold.

[0029] In the implementation of this application embodiment, under normal operating conditions, even without replacement of the monitoring point, the change in the value of each monitoring point between adjacent periods will exhibit random fluctuations due to measurement errors and slight real deformation. The amplitude of these fluctuations varies from monitoring point to monitoring point; monitoring points with good geological conditions and stable installations exhibit smaller fluctuations, while those located in soft soil areas and subject to significant construction disturbances exhibit larger fluctuations. The standard deviation of the change data of the monitoring point over multiple consecutive observation periods prior to the current observation period is a quantitative characterization of this fluctuation amplitude under normal operating conditions, representing the inherent variation amplitude of the monitoring point under historical normal conditions.

[0030] In the embodiments of this application, the number of consecutive observation periods is usually no less than 5. The reason is that in statistics, the calculation of standard deviation requires a sufficient sample size to be statistically significant. According to the central limit theorem, the larger the sample size, the more accurate the estimate of the population standard deviation by the sample standard deviation. Therefore, in specific implementation, the historical change data of the monitoring point in the most recent 5 to 10 periods are usually selected as the calculation window, which can both ensure statistical stability and reflect the recent fluctuation characteristics.

[0031] In the embodiments of this application, the changes in adjacent periods of the monitoring points follow a random distribution centered on the actual deformation and measured by the standard deviation σ. The vast majority of changes fall within a certain range. When the change in a certain period exceeds the upper limit, it means that the change in that period exceeds the reasonable range of normal fluctuations and is a low-probability event. Physically, it is very likely to correspond to some abnormal working condition. The k value is generally selected as greater than or equal to 4.

[0032] In this embodiment, the mutation judgment threshold is dynamically adaptive data. Each monitoring point independently calculates the threshold based on the standard deviation of its own historical data. Points with good stability have a small threshold, while points with poor stability have a large threshold, realizing personalized detection sensitivity configuration. For monitoring points with different geological conditions and different burial quality, the system can automatically adapt without manual adjustment of parameters one by one. At the same time, as the project progresses, the stability of the monitoring points may change, and the fluctuation range of the change may evolve over time. Through the sliding window mechanism of this embodiment, the standard deviation is always calculated based on the most recent historical data, which can automatically adjust the threshold to follow the slow evolution of the stability state of the monitoring points, maintaining the timeliness and accuracy of detection.

[0033] In one possible implementation, validating the observation period for the re-embedded monitoring points includes: When at least two monitoring points within the target engineering area are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a global event. When there are no at least two monitoring points in the target project area that are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a local event; The observation period for re-burying suspected monitoring points corresponding to local events is taken as the actual re-burying observation period; the observation period for re-burying suspected monitoring points corresponding to global events is taken as a false mutation caused by factors other than monitoring point replacement.

[0034] In the implementation of this application embodiment, based on the above identification results, a consistency comparison verification mechanism of spatial dimension is introduced. By analyzing the spatial distribution characteristics of multiple abrupt changes within the same target engineering area, false abrupt changes caused by non-measuring point replacement reasons such as benchmark point changes, systematic instrument errors, and regional environmental factors are effectively eliminated. In actual deformation monitoring engineering, the damage to a monitoring point is usually caused by local factors. The point happens to be located on the vehicle passage path and is crushed, touched by construction machinery, or buried due to partial collapse of the foundation pit. Such damage events only affect the specific monitoring point, and other adjacent monitoring points are not affected. Therefore, the re-burying event is spatially isolated and only involves a single monitoring point.

[0035] In this embodiment of the application, compared with the existing method of anomaly detection based solely on the data sequence of a single measuring point, spatial dimension evidence is introduced. By examining the synchronous behavior of multiple monitoring points within the same engineering area, the cause of the mutation is inferred. This dimensional verification architecture fully utilizes the spatial correlation of deformation monitoring data, so that the identification of false mutations no longer relies on the user's subjective experience judgment, but rather the algorithm automatically completes the explanation of the cause of the mutation, whether it is a local independent measuring point replacement or a global systemic deviation, thus realizing the automatic differentiation of the cause of the event.

[0036] In one possible implementation, calculating the cumulative change of each monitoring point in subsequent observation periods based on the actual re-burying observation period includes: When the current observation period is later than the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period is calculated, plus the difference between the current monitoring value and the first monitoring value after the actual re-burying observation period, as the cumulative change of the current observation period. When the current observation period is equal to the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period shall be used as the cumulative change of the current observation period.

[0037] In the implementation of this application, a specific calculation method for the cumulative change before and after the re-burying of the measuring point is given. After obtaining the actual re-burying observation period through the above scheme, the system automatically switches the calculation logic accordingly and completes the benchmark conversion and accurate splicing of the monitoring data of the old and new measuring points. Its core objective is to keep the cumulative change sequence before and after re-burying continuous and physically consistent. Its essence is the switching and connection of the cumulative calculation benchmark after the change of physical entity. When the physical measuring point is replaced, the calculation of the cumulative change needs to switch from the benchmark based on the first period of the old measuring point to the benchmark based on the first period of the new measuring point, and complete the equivalent conversion of the cumulative amount under the old and new benchmarks through corresponding mathematical processing. In this application embodiment, a two-segment calculation model is established, which inherits the historical value of the cumulative amount before re-burying and continuously accumulates the subsequent increments after re-burying. This realizes the accurate splicing of the cumulative deformation between the old and new physical measuring points, which not only retains the historical deformation information of the old measuring point, but also accurately reflects the subsequent changes of the new measuring point, fundamentally solving the problem of calculation error of cumulative change caused by the replacement of measuring points in the prior art.

[0038] Based on the same inventive concept, this application also provides a deformation monitoring data analysis system, including: The acquisition unit is configured to acquire a sequence of monitoring data for each deformation monitoring point within the target engineering area over multiple consecutive observation periods; the monitoring data sequence includes horizontal displacement data and vertical displacement data. The calculation unit is configured to calculate the horizontal and vertical changes between adjacent observation periods for each monitoring point; The detection unit is configured to perform multi-dimensional joint mutation detection based on the horizontal and vertical changes to identify the observation period for suspected re-embedding of monitoring points; the observation period for suspected re-embedding of monitoring points is the observation period during which the corresponding monitoring point has a risk of re-embedding. The verification unit is configured to verify the validity of the re-embedding observation period of the suspected measuring point, exclude false changes caused by factors other than the replacement of the monitoring point, and take the final re-embedding observation period of the suspected measuring point as the actual re-embedding observation period of the corresponding monitoring point. The cumulative unit is configured to calculate the cumulative change of each monitoring point in subsequent observation periods based on the actual re-burying observation period.

[0039] In one possible implementation, the detection unit is further configured as follows: When both the horizontal and vertical changes at the monitoring point are greater than the corresponding preset abrupt change thresholds, the observation period corresponding to the horizontal and vertical changes is determined to be the observation period for the suspected monitoring point to be re-buried.

[0040] In one possible implementation, the horizontal change includes a first horizontal change and a second horizontal change orthogonal to the first horizontal change. The detection unit is further configured to: The standard deviations of the first horizontal change, the second horizontal change, and the vertical change were obtained from multiple consecutive observation periods prior to the observation period and used as reference standard deviations for the changes in different directions. The reference standard deviation is multiplied by a factor to form the mutation determination threshold.

[0041] In one possible implementation, the verification unit is further configured as follows: When at least two monitoring points within the target engineering area are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a global event. When there are no at least two monitoring points in the target project area that are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a local event; The observation period for re-burying suspected monitoring points corresponding to local events is taken as the actual re-burying observation period; the observation period for re-burying suspected monitoring points corresponding to global events is taken as a false mutation caused by factors other than monitoring point replacement.

[0042] In one possible implementation, the accumulation unit is further configured as follows: When the current observation period is later than the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period is calculated, plus the difference between the current monitoring value and the first monitoring value after the actual re-burying observation period, as the cumulative change of the current observation period. When the current observation period is equal to the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period shall be used as the cumulative change of the current observation period.

[0043] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0044] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices or units, or may be electrical, mechanical or other forms of connection.

[0045] The units described as separate components may or may not be physically separate. As will be apparent to those skilled in the art, the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0046] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0047] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0048] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for analyzing deformation monitoring data, characterized in that, include: Acquire monitoring data sequences for each deformation monitoring point within the target engineering area over multiple consecutive observation periods; the monitoring data sequences include horizontal displacement data and vertical displacement data; For each monitoring point, calculate the horizontal and vertical changes between adjacent observation periods; Multidimensional joint mutation detection is performed based on the horizontal and vertical changes to identify the observation period for suspected re-embedding of monitoring points; the observation period for suspected re-embedding of monitoring points is the observation period during which the corresponding monitoring point has the risk of re-embedding. The validity of the observation period for the re-embedding of the suspected measuring points was verified to exclude false abrupt changes caused by factors other than the replacement of the monitoring points, and the final observation period for the re-embedding of the suspected measuring points was taken as the actual observation period for the re-embedding of the corresponding monitoring points. The cumulative change of each monitoring point in the subsequent observation period is calculated based on the actual re-burying observation period.

2. The deformation monitoring data analysis method according to claim 1, characterized in that, The observation period for identifying suspected measuring points and re-burying them includes: When both the horizontal and vertical changes at the monitoring point are greater than the corresponding preset abrupt change thresholds, the observation period corresponding to the horizontal and vertical changes is determined to be the observation period for the suspected monitoring point to be re-buried.

3. The deformation monitoring data analysis method according to claim 2, characterized in that, The horizontal change includes a first horizontal change and a second horizontal change that is orthogonal to the first horizontal change. The observation period for identifying suspected measuring points and re-burying them also includes: The standard deviations of the first horizontal change, the second horizontal change, and the vertical change were obtained from multiple consecutive observation periods prior to the observation period and used as reference standard deviations for the changes in different directions. The reference standard deviation is multiplied by a factor to form the mutation determination threshold.

4. The deformation monitoring data analysis method according to claim 1, characterized in that, The effectiveness verification of the observation period for the re-embedded suspected measuring points includes: When at least two monitoring points within the target engineering area are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a global event. When there are no at least two monitoring points in the target project area that are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a local event; The observation period for re-burying suspected monitoring points corresponding to local events is taken as the actual re-burying observation period; the observation period for re-burying suspected monitoring points corresponding to global events is taken as a false mutation caused by factors other than monitoring point replacement.

5. The deformation monitoring data analysis method according to claim 1, characterized in that, The cumulative change of each monitoring point in the subsequent observation period, calculated based on the actual re-burying observation period, includes: When the current observation period is later than the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period is calculated, plus the difference between the current monitoring value and the first monitoring value after the actual re-burying observation period, as the cumulative change of the current observation period. When the current observation period is equal to the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period shall be used as the cumulative change of the current observation period.

6. A deformation monitoring data analysis system, characterized in that, include: The acquisition unit is configured to acquire a sequence of monitoring data for each deformation monitoring point within the target engineering area over multiple consecutive observation periods; the monitoring data sequence includes horizontal displacement data and vertical displacement data. The calculation unit is configured to calculate the horizontal and vertical changes between adjacent observation periods for each monitoring point; The detection unit is configured to perform multi-dimensional joint mutation detection based on the horizontal and vertical changes to identify the observation period for suspected re-embedding of monitoring points; the observation period for suspected re-embedding of monitoring points is the observation period during which the corresponding monitoring point has a risk of re-embedding. The verification unit is configured to verify the validity of the re-embedding observation period of the suspected measuring point, exclude false changes caused by factors other than the replacement of the monitoring point, and take the final re-embedding observation period of the suspected measuring point as the actual re-embedding observation period of the corresponding monitoring point. The cumulative unit is configured to calculate the cumulative change of each monitoring point in subsequent observation periods based on the actual re-burying observation period.

7. The deformation monitoring data analysis system according to claim 6, characterized in that, The detection unit is further configured to: When both the horizontal and vertical changes at the monitoring point are greater than the corresponding preset abrupt change thresholds, the observation period corresponding to the horizontal and vertical changes is determined to be the observation period for the suspected monitoring point to be re-buried.

8. The deformation monitoring data analysis system according to claim 7, characterized in that, The horizontal change includes a first horizontal change and a second horizontal change that is orthogonal to the first horizontal change. The detection unit is further configured to: The standard deviations of the first horizontal change, the second horizontal change, and the vertical change were obtained from multiple consecutive observation periods prior to the observation period and used as reference standard deviations for the changes in different directions. The reference standard deviation is multiplied by a factor to form the mutation determination threshold.

9. A deformation monitoring data analysis system according to claim 6, characterized in that, The verification unit is further configured to: When at least two monitoring points within the target engineering area are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a global event. When there are no at least two monitoring points in the target project area that are simultaneously in the suspected re-burying observation period, the suspected re-burying observation period of the monitoring point is determined to be a local event; The observation period for re-burying suspected monitoring points corresponding to local events is taken as the actual re-burying observation period; the observation period for re-burying suspected monitoring points corresponding to global events is taken as a false mutation caused by factors other than monitoring point replacement.

10. A deformation monitoring data analysis system according to claim 6, characterized in that, The accumulation unit is further configured as follows: When the current observation period is later than the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period is calculated, plus the difference between the current monitoring value and the first monitoring value after the actual re-burying observation period, as the cumulative change of the current observation period. When the current observation period is equal to the actual re-burying observation period, the cumulative change of the last period before the actual re-burying observation period shall be used as the cumulative change of the current observation period.

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