Method and system for automatically measuring deformation of large-area foundation pit supporting structure

Abstract

The invention relates to an automatic measurement method and system for deformation of a large-area foundation pit supporting structure. The method comprises the following steps: performing synchronous data acquisition processing on multiple types of monitoring point groups arranged according to the large-area foundation pit supporting and protecting structure to obtain an initial measurement data set for representing the deformation state of the supporting and protecting structure; based on a time sequence division mode taking a construction working condition change node as an index, performing staged grouping processing on the initial measurement data set to obtain a dynamic measurement curve set for the deformation evolution process of the supporting and protecting structure in different construction stages; and according to the dynamic measurement curve set, performing pattern recognition processing on the deformation behavior of the supporting and protecting structure in the whole construction period to obtain a multi-dimensional deformation feature set for describing the overall deformation feature and the local deformation feature of the supporting and protecting structure. By the adoption of the method, accurate recognition and reliable analysis of the deformation rule and the abnormal deformation phenomenon of the large-area foundation pit supporting structure can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building, in particular to a method and system for automatically measuring deformation of a large-area foundation pit support structure. BACKGROUND

[0002] In the field of building technology, a support structure is used in a large-area foundation pit to ensure the safety of the construction process of the large-area foundation pit.

[0003] In related methods for measuring deformation of a support structure of a large-area foundation pit, data acquisition of the deformation state of the support structure is achieved by means of fixed-point and regular manual inspection or independent acquisition based on a single-point monitoring device. However, this method has the problems of poor data continuity, insufficient timeliness, and lack of correspondence during the construction phase, which results in the inability to timely and comprehensively reflect the deformation evolution characteristics of the support structure during the entire construction process, and limits the ability to identify abnormal deformation trends in advance and dynamically adjust construction decisions. SUMMARY

[0004] Therefore, it is necessary to provide a method and system for automatically measuring deformation of a large-area foundation pit support structure, a computer device, and a computer readable storage medium to improve the accurate identification and reliable analysis of the deformation law and abnormal deformation phenomenon of the large-area foundation pit support structure.

[0005] In a first aspect, the present application provides a method for automatically measuring deformation of a large-area foundation pit support structure, comprising: synchronously collecting and processing a plurality of monitoring point groups arranged according to the large-area foundation pit support structure to obtain an initial measurement data set for representing the deformation state of the large-area foundation pit support structure, wherein the plurality of monitoring point groups include displacement monitoring points, internal force monitoring points, and environmental monitoring points; performing stage grouping processing on the initial measurement data set based on a time series division method with construction condition change nodes as indexes to obtain a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support structure for different construction stages; performing pattern recognition processing on the deformation behavior of the large-area foundation pit support structure during the entire construction cycle according to the dynamic measurement curve set to obtain a multi-dimensional deformation feature set for describing the overall deformation characteristics and local deformation characteristics of the large-area foundation pit support structure, wherein the multi-dimensional deformation feature set is used to realize stage evaluation, abnormal trend early warning, and construction control decision of the deformation state of the large-area foundation pit support structure.

[0006] In a second aspect, the present application further provides a system for automatically measuring deformation of a large-area foundation pit support structure, comprising: The collection module is configured to perform synchronous data collection and processing on a plurality of monitoring point groups arranged according to the large-area foundation pit support and protection structure, to obtain an initial measurement data set for representing the deformation state of the large-area foundation pit support and protection structure, wherein the plurality of monitoring point groups include displacement monitoring points, internal force monitoring points and environmental monitoring points; The curve fitting module is configured to perform stage grouping processing on the initial measurement data set based on a time sequence division mode indexed by construction condition change nodes, to obtain a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support and protection structure in different construction stages. The pattern recognition module is configured to perform pattern recognition processing on the deformation behavior of the large-area foundation pit support and protection structure in the whole construction cycle according to the dynamic measurement curve set, to obtain a multi-dimensional deformation feature set for describing the overall deformation characteristics and local deformation characteristics of the large-area foundation pit support and protection structure, and the multi-dimensional deformation feature set is used to realize stage evaluation, abnormal trend early warning and construction control decision of the deformation state of the large-area foundation pit support and protection structure.

[0007] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor realizes the above steps when executing the computer program.

[0008] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the above steps.

[0009] The automatic measurement method, system, computer device and computer readable storage medium for the deformation of the large-area foundation pit support and protection structure can firstly perform synchronous data collection and processing on the plurality of monitoring point groups, to obtain the initial measurement data set for representing the deformation state of the large-area foundation pit support and protection structure, thereby ensuring the spatio-temporal consistency and integrity of the measurement data of the monitoring points; secondly, the initial measurement data set is processed by stage grouping according to the time sequence division mode indexed by the construction condition change nodes, to obtain the dynamic measurement curve set, thereby revealing the deformation change process of the support and protection structure with the construction progress evolution in stages; thirdly, the dynamic measurement curve set is processed by pattern recognition, to obtain the multi-dimensional deformation feature set for describing the overall deformation characteristics and local deformation characteristics, thereby realizing comprehensive description of the deformation behavior of the support and protection structure in the whole construction cycle; based on this, the accurate identification and reliable analysis of the deformation law and abnormal deformation phenomenon of the large-area foundation pit support and protection structure are improved, and effective and reliable data support is provided for the stage evaluation, abnormal trend early warning and construction control decision of the deformation state of the large-area foundation pit support and protection structure. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings needed to be used in the embodiments or the related art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0011] Figure 1 The flowchart of the automatic measurement method of the deformation of the large-area foundation pit support and protection structure in an embodiment; Figure 2 The structural block diagram of the automatic measurement system of the deformation of the large-area foundation pit support and protection structure in an embodiment. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solutions and advantages of the present application more clear, the following will further describe the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0013] In one embodiment, as shown in Figure 1 , an automatic measurement method of the deformation of a large-area foundation pit support and protection structure is provided. In this embodiment, the method is applied to a server for illustration. It should be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and can be realized through the interaction of the terminal and the server. In this embodiment, the method includes the following steps S101 to S103.

[0014] Step S101, synchronously collecting and processing a plurality of monitoring point groups arranged according to a large-area foundation pit support and protection structure to obtain an initial measurement data set for representing the deformation state of the large-area foundation pit support and protection structure, the plurality of monitoring point groups including displacement monitoring points, internal force monitoring points and environmental monitoring points.

[0015] Wherein, the large-area foundation pit support and protection structure represents a large-scale support and reinforcement system arranged to prevent soil collapse, foundation pit deformation or damage to the surrounding environment during underground space excavation construction, for maintaining the stability of the foundation pit enclosure system during excavation, such as a large-scale internal support system arranged in deep foundation pit engineering, including continuous wall, steel support, pile anchor system and other structures.

[0016] Wherein, the plurality of monitoring point groups represent a plurality of monitoring point sets with different detection functions arranged in the large-area foundation pit support and protection structure and its surrounding area, for real-time acquisition of foundation pit structure state and environmental change information from different angles, including displacement monitoring points, internal force monitoring points and environmental monitoring points.

[0017] Among them, the displacement type monitoring point represents a monitoring unit specially used for measuring the spatial displacement change of the support supporting structure or the surrounding soil during excavation, so as to obtain the deformation amount, deformation direction and other data of the support supporting structure, such as the displacement sensor or inclinometer installed on the support beam node or the surface of the foundation pit slope.

[0018] Among them, the internal force type monitoring point represents a monitoring unit used for measuring the force change inside the support supporting structure or inside the support member, so as to obtain the force state of the support supporting structure in different construction stages, such as the stress meter installed on the steel support for measuring the change of axial force or the strain meter buried in the support pile body.

[0019] Among them, the environmental monitoring point represents a monitoring unit used for monitoring the change of external natural environment parameters affecting the deformation behavior of the support supporting structure, so as to obtain environmental data such as temperature, humidity, rainfall, underground water level, etc., such as the automatic weather station arranged on the top of the foundation pit slope or the underground water level monitoring well arranged around the foundation pit.

[0020] Among them, the initial measurement data set represents the original data set which is completely described the deformation state of the large-area foundation pit support supporting structure after the measurement data of the multiple types of monitoring points are synchronously collected and arranged in a unified format.

[0021] Exemplarily, first, a plurality of monitoring points including displacement type monitoring points, internal force type monitoring points and environmental type monitoring points are set for the large-area foundation pit support supporting structure, wherein the displacement type monitoring points are used to detect the deformation amount of the support supporting structure at different time periods and different spatial positions, the internal force type monitoring points are used to detect the stress change inside the support supporting structure, and the environmental type monitoring points are used to record the potential influence of external environmental factors such as temperature, humidity and rainfall on the state of the support supporting structure. Further, the multiple types of monitoring points are synchronously collected and processed to ensure that the data has time consistency and spatial correlation, that is, the numerical information of each type of monitoring point in the current state is collected at a unified sampling time and at a unified sampling frequency.

[0022] Further, after the collection is completed, all the collected data need to be preliminarily arranged, including data format standardization, invalid value elimination, sampling time stamp alignment and other processing, so as to form an initial measurement data set which is clear in structure and coherent in time sequence; the initial measurement data set completely covers the quantitative representation of the displacement level, the internal force level and the environmental level at each monitoring point and each time node.

[0023] Step S102, based on the time sequence division mode indexed by the construction condition change node, the initial measurement data set is grouped and processed in stages to obtain a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support supporting structure for different construction stages.

[0024] Among them, the construction condition change node represents a specific time point at which the construction operation content, the construction area state or the support and protection structure changes significantly in the process of foundation pit excavation and support construction, which is used as a reference basis for data stage division, such as the completion of support beam installation, the excavation of foundation pit to a certain design depth, the completion of enclosure wall grouting reinforcement and other key nodes.

[0025] Among them, the construction phase represents a work phase unit with relatively uniform construction activity characteristics in a specific time period divided by the construction condition change node, which is used to describe the stress and deformation evolution process of the large-area foundation pit support and protection structure in different work processes, such as from the initial excavation of the foundation pit to the completion of the first support installation as a construction phase, and from the completion of the first support installation to the completion of the second excavation as another construction phase.

[0026] Among them, the dynamic measurement curve set represents the dynamic evolution trajectory of displacement, internal force and environmental parameters of the large-area foundation pit support and protection structure in each construction phase, such as the stress change curve set of the support axial force gradually increasing with the increase of the excavation depth, or the deformation curve set of the horizontal displacement of the top of the foundation pit slope gradually increasing with time.

[0027] Exemplarily, first, the construction condition change nodes actually occurred in the construction process are used as time indexes to node the initial measurement data set on the time axis, that is, the corresponding data index is marked at the position of each construction condition change node. Subsequently, according to these construction condition change nodes, continuous time intervals are demarcated, and the initial measurement data set is divided into several data sub-sets with clear time boundaries, each data sub-set corresponding to the measurement data record in a construction phase; this division method can make the measurement data records in different construction phases independent of each other, facilitating the analysis of the deformation evolution change of the support and protection structure in each construction phase. Further, after data segmentation, the data sub-set in each construction phase is further processed, that is, the curves of each data sub-set changing with time are plotted according to the time sequence to form a dynamic measurement curve set; through this processing method, the dynamic change process of the displacement change, internal force change and environmental change of the large-area foundation pit support and protection structure in different construction phases can be clearly presented.

[0028] Step S103, according to the dynamic measurement curve set, the deformation behavior of the large-area foundation pit support and protection structure in the whole construction cycle is processed for pattern recognition, and a multi-dimensional deformation feature set for describing the overall deformation characteristics and local deformation characteristics of the large-area foundation pit support and protection structure is obtained, which is used to realize the stage evaluation, abnormal trend early warning and construction control decision of the deformation state of the large-area foundation pit support and protection structure.

[0029] wherein the construction whole cycle represents a complete time range experienced during the period from the start of the large-area foundation pit support and protection structure construction activity to the completion of all construction tasks, for comprehensively covering the change process of the deformation behavior of the support and protection structure in various construction stages, such as all construction time periods contained from the start of the first excavation of the foundation pit to the end of the removal of the last layer of support.

[0030] wherein the multi-dimensional deformation feature set is used to comprehensively describe a set of quantitative parameter sets with different feature dimensions of the deformation characteristics of the support and protection structure, for describing the deformation state of the support and protection structure as a whole and locally from multiple angles.

[0031] wherein the overall deformation characteristics represent attribute features reflecting the overall spatial form change trend of the large-area foundation pit support and protection structure as a whole from a macroscopic scale within the construction whole cycle, for identifying the overall stability and deformation trend of the whole support system, such as the horizontal displacement or vertical settlement mode of the whole periphery of the foundation pit top.

[0032] wherein the local deformation characteristics represent specific deformation behavior features occurring in some local areas or member positions of the large-area foundation pit support and protection structure from a local scale within the construction whole cycle, for identifying phenomena such as local stress abnormality and local damage risk, such as the sharp change of internal force of a certain steel support in a certain construction stage or the sudden displacement increase of a local area of a wall.

[0033] wherein the stage-by-stage evaluation represents a stage-by-stage, quantitative determination and analysis of the change of the deformation state of the large-area foundation pit support and protection structure in different construction stages, for timely grasping the deformation control level of the support system in each construction stage, such as independently evaluating the stability of the foundation pit after the completion of the support to determine whether the next excavation stage can be entered.

[0034] wherein the abnormal trend early warning represents the early discovery of potential abnormal development signs existing in the deformation behavior of the large-area foundation pit support and protection structure, for providing preventive control measures for construction management personnel, such as timely issuing an alarm and taking reinforcement measures when detecting a local displacement accelerated growth trend.

[0035] wherein the construction control decision represents a construction adjustment scheme or emergency control scheme formulated for the current state of the large-area foundation pit support and protection structure, for guaranteeing the structural safety in the construction process and the smooth progress of the construction plan, such as adjusting the support installation sequence or densifying the monitoring point layout according to the local settlement evaluation results.

[0036] Exemplarily, firstly, around the time range of the whole construction cycle, important feature information reflecting the deformation state change of the support and protection structure is extracted from the dynamic measurement curve set, so as to subsequently identify the regularity. Further, based on the extracted important feature information, the mode recognition processing is further performed on various measurement curves in different construction stages in the dynamic measurement curve set, that is, by inducing the common characteristics of the deformation trend and the change amplitude of each measurement curve, the overall deformation characteristics and the local deformation characteristics of the large-area foundation pit support and protection structure are extracted, thereby forming a multi-dimensional deformation feature set comprehensively describing the deformation evolution characteristics of the large-area foundation pit support and protection structure in each construction stage in the whole construction cycle, so as to provide basic data basis and analysis reference for subsequent stage evaluation, abnormal trend early warning and construction control decision.

[0037] In the automatic measurement method of the deformation of the large-area foundation pit support and protection structure, firstly, the synchronous data acquisition and processing are performed on various monitoring point groups, and an initial measurement data set representing the deformation state of the large-area foundation pit support and protection structure is obtained, so as to ensure the spatiotemporal consistency and integrity of the measurement data of various monitoring points; further, according to the time sequence division mode with the construction condition change node as the index, the initial measurement data set is grouped and processed in stages, and a dynamic measurement curve set is obtained, so as to reveal the deformation change process of the support and protection structure evolving with the construction progress in stages; further, the mode recognition processing is performed on the dynamic measurement curve set, and a multi-dimensional deformation feature set for describing the overall deformation characteristics and the local deformation characteristics is obtained, so as to comprehensively describe the deformation behavior of the support and protection structure in the whole construction cycle; based on this, the accurate identification and reliable analysis of the deformation law and abnormal deformation phenomenon of the large-area foundation pit support and protection structure are improved, and effective and reliable data support is provided for the stage evaluation, abnormal trend early warning and construction control decision of the deformation state of the large-area foundation pit support and protection structure.

[0038] In one exemplary embodiment, based on the time sequence division mode with the construction condition change node as the index, the initial measurement data set is grouped and processed in stages, and a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support and protection structure for different construction stages is obtained, including steps S201 to S203.

[0039] In step S201, according to the construction condition change nodes reflected by each construction condition change time recorded in the construction log, the node identification processing is performed on the initial measurement data set, and a node index set corresponding to the large-area foundation pit support and protection structure excavation completion node, the support removal node and the support replacement completion node is obtained.

[0040] The construction log represents a detailed construction activity schedule and operation content file recorded by a construction unit during large-area foundation pit construction, for example, a document recording the specific time when the foundation pit excavation depth reaches a certain design elevation and the operation description.

[0041] The node index set represents a set of index data for identifying various construction condition change nodes, for example, setting index numbers on the initial measurement data set at the time points when excavation is completed, support is removed, and replacement support is completed.

[0042] The excavation completion node represents a construction condition change node when a certain stage of excavation task is completed in the construction process, which is used to define the end point of an important construction interval, for example, the time point when the foundation pit bottom is excavated to the design elevation and is ready for support installation.

[0043] The support removal node represents a construction condition change node when the original support member completes the removal operation in the construction process, which is used to mark the time point of the local stress state change of the support and protection structure, for example, the time point when the previous temporary support is removed before further excavation or replacement support work of the foundation pit.

[0044] The replacement support completion node represents a construction condition change node after the replacement and installation of a new support component and the completion of the fixing operation in the construction process, which is used to mark the time point when the support and protection structure re-forms a complete stress system, for example, the time point when the new support component is used to re-strengthen after the original support fails.

[0045] For example, first, according to the construction activity progress information recorded in detail in the construction log, the construction condition change time points directly related to the state change of the large-area foundation pit support and protection structure are extracted; the construction log usually records specific events such as excavation completion, support installation or removal, and replacement support completion in chronological order, so these specific events can be used as a basis to establish nodes with clear physical meaning in the construction process, i.e., construction condition change nodes. Subsequently, these construction condition change nodes are mapped to the measurement data of the initial measurement data set to perform node identification processing on the measurement data in the initial measurement data set; during the node identification processing, the measurement data sampling time points corresponding to each construction condition change node are accurately located in the measurement data corresponding to the initial measurement data set, and the measurement data corresponding to these sampling time points are distinguished by adding marks or index numbers to form a node index set; based on this, each index record in the node index set records the association between a construction condition change node and the corresponding measurement data in the initial measurement data set.

[0046] In step S202, the initial measurement data set is divided into stage intervals at consecutive construction condition change nodes according to the node index set, to obtain a stage monitoring data set bounded by adjacent construction condition change nodes, which includes monitoring data of each construction stage.

[0047] The stage monitoring data set represents a data set formed by dividing the initial measurement data set into stages according to the node index set, corresponding to the monitoring data sub-set collected continuously in each construction stage, and used to describe the deformation evolution process of the support structure, such as the data set composed of all displacement, internal force and environmental monitoring data recorded during the period from excavation completion to support installation completion.

[0048] For example, the initial measurement data set is divided into stage intervals based on the node index set, that is, each measurement data in the initial measurement data set is divided into several continuous but non-overlapping data sub-sets along the time axis, bounded by two adjacent construction condition change nodes in the node index set. The stage monitoring data set is obtained by combining each data sub-set, thereby converting the initial measurement data set containing each measurement data into the stage monitoring data set containing monitoring data of each construction stage. Correspondingly, the detection data can represent the data obtained by classifying the initial measurement data into a specified construction stage in the stage monitoring data set. Each data sub-set corresponds to a construction stage and contains displacement changes, internal force changes and environmental changes of all monitoring points collected in the construction stage. In addition, during the data division process, the starting time and ending time of the monitoring data of each construction stage in the stage monitoring data set should strictly correspond to the matched construction condition change nodes, to avoid time overlap or data omission. Based on this, the complex initial measurement data set spanning the entire construction cycle can be divided into several data units with clear structure and clear construction stage characteristics, providing logical and pure data support for subsequent deformation evolution process modeling and analysis.

[0049] In step S203, the monitoring data of each construction stage in the stage monitoring data set is subjected to curve fitting processing, to obtain a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support structure in different construction stages.

[0050] Exemplarily, the monitoring data of each construction stage in the stage monitoring data set is subjected to curve fitting processing, that is, in the curve fitting processing, the change curves of various monitoring points are drawn with time as the horizontal axis and the corresponding monitoring index (such as displacement, internal force value, specified environmental parameter, etc.) as the vertical axis, and a suitable mathematical fitting method is adopted to model the curve shape, thereby generating a measurement curve that is continuous, smooth and can accurately reflect the actual change trend. Furthermore, the measurement curve fitted in different construction stages should be labeled with the corresponding construction stage and monitoring index, so that the curve information can be traced back to the specific construction stage and physical meaning based on subsequent analysis. Based on this, the discrete monitoring data in the stage monitoring data set can be converted into a change track with continuous time sequence characteristics, thereby forming a dynamic measurement curve set covering the deformation evolution process of the large-area foundation pit support and protection structure in each construction stage.

[0051] In this embodiment, first, the initial measurement data set is subjected to node identification processing of the construction condition change node, so that the key time nodes in the construction process can be determined to obtain a node index set, ensuring the time sequence accuracy of subsequent data division; Furthermore, the initial measurement data set is subjected to stage interval division processing according to the node index set, so that a stage monitoring data set corresponding to the construction stage change can be formed, ensuring the independence and continuity of the divided data; Furthermore, the stage monitoring data set is subjected to curve fitting processing, so that a dynamic measurement curve set covering the deformation evolution process of the large-area foundation pit support and protection structure in each construction stage can be obtained, based on which the accurate division and dynamic continuous modeling of the measurement data of the support and protection structure in the whole construction cycle are realized.

[0052] In one exemplary embodiment, the initial measurement data set is subjected to stage interval division processing at the continuous construction condition change nodes according to the node index set, to obtain a stage monitoring data set bounded by adjacent construction condition change nodes, including steps S301 to S303.

[0053] Step S301, according to the time stamp field corresponding to each construction condition change node, the node index set is subjected to sequential arrangement processing, to obtain a continuous construction condition change node corresponding ordered time sequence set, the ordered time sequence set includes the node index identification corresponding to the continuous time section.

[0054] Among them, the time stamp field corresponding to the construction condition change node represents the time information field used to record the specific occurrence time of each construction condition change node in the node index set, which is used to support the construction condition change node to be arranged in time sequence.

[0055] The ordered time sequence set represents a node set arranged in time sequence by sequentially arranging the time stamp fields of the construction condition change nodes, and is used to define the time sections of each continuous construction stage, such as the time sequence composed of sequentially arranged excavation completion nodes, support removal nodes, and support replacement completion nodes.

[0056] The time section represents a continuous time range determined by two adjacent construction condition change nodes on the time axis in the ordered time sequence set, such as a time range between an excavation completion node and an adjacent support removal node.

[0057] The node index identifier represents an independent index number assigned to each construction condition change node, and is used to quickly identify and locate the corresponding construction condition change node during data processing.

[0058] For example, first, each node index in the node index set records a construction condition change node and a corresponding time stamp field, so that all construction condition change nodes can be arranged in time sequence according to the time stamp fields of the construction condition change nodes, thereby generating a continuous and ordered construction condition change node time sequence set, in which each pair of adjacent construction condition change nodes defines a start and end time section of a construction stage. The time sequence not only reflects the natural evolution of the construction process, but also provides a clear time limit basis for subsequent monitoring data division according to the construction logic. Furthermore, during the arrangement process, the node index should not cross or miss on the time axis to ensure the continuity and completeness of the time sequence set, thereby forming an ordered time section corresponding to the continuous construction condition change process. Finally, the obtained ordered time sequence set is divided into time sections, each time section corresponds to two adjacent construction condition change nodes, and is marked by a node index identifier, so that the extraction of measurement data of each construction stage can be directly located and grouped according to the time section, and the basis relationship between construction stage division and time data mapping is established.

[0059] In step S302, each measurement data in the initial measurement data set is matched to the node index identifier of the corresponding time section in the ordered time sequence set according to the time label of each measurement data in the initial measurement data set, and each stage monitoring data subset divided according to the continuous time section is obtained.

[0060] The time label of the measurement data represents the collection time information attached to each measurement data record.

[0061] Among them, the phased monitoring data sub-set represents the data sub-set formed by merging the measurement data belonging to the same construction phase in the initial measurement data set according to the time section division, for example, all measurement data from the completion of support installation to the completion of the next excavation are attributed to a certain phased monitoring data sub-set.

[0062] Exemplarily, each measurement data in the initial measurement data set is matched based on the ordered time sequence set, specifically, each measurement data in the initial measurement data set carries a corresponding time label, which can be compared one by one with each time section in the ordered time sequence set by reading the time label, that is, in the comparison process, each measurement data should be attributed to which continuous time section corresponding to the construction phase according to the time size relationship. Based on this, a large amount of measurement data originally spanning the entire construction period can be accurately divided into each continuous construction phase according to the time zoning standard of construction progress, thereby forming a phased monitoring data sub-set organized according to time sections; each phased monitoring data sub-set only contains data records collected in the corresponding construction phase, and different phased monitoring data sub-sets do not overlap, maintaining the independence and continuity in time.

[0063] Step S303, according to the hierarchical index mapping structure established based on the key-value pair of node index identifier and measurement data identifier, each phased monitoring data sub-set is subjected to composite index processing to obtain a phased monitoring data set bounded by adjacent construction condition change nodes.

[0064] Among them, the measurement data identifier represents the identifier information for uniquely identifying each measurement data in the initial measurement data set.

[0065] Among them, the hierarchical index mapping structure represents a hierarchical retrieval system established based on the combination of node index identifier and measurement data identifier, which is used to organize the phased monitoring data sub-set and realize efficient access according to the dual dimensions of construction phase and measurement data, for example, the upper layer is classified by node index identifier, and the lower layer points to specific data by measurement data identifier.

[0066] Exemplarily, based on obtaining the various phased monitoring data subsets divided by time segments, it is necessary to further establish an effective index management mechanism to support subsequent quick retrieval and analysis of the phased data. Specifically, by constructing a hierarchical index mapping structure based on the combination relationship of the node index identifier and the measurement data identifier, the hierarchical index mapping structure takes the measurement data identifier of the construction condition change node as the upper layer classification identifier, and takes the measurement data identifier of each measurement data as the lower layer retrieval unit, thereby organizing the various phased monitoring data subsets into a composite index system with unified retrieval logic. Finally, through the above composite index processing of the various phased monitoring data subsets, a phased monitoring data set with adjacent construction condition change nodes as boundaries, complete content and efficient retrieval is obtained.

[0067] In this embodiment, first, the node index set is sequentially arranged to obtain an ordered time sequence set, so that the time sequence relationship between the construction condition change nodes can be determined, and the continuity and time sequence logic of the stage division are ensured. Then, the time tag of each measurement data in the initial measurement data set is matched with the ordered time sequence set to obtain the phased monitoring data subsets divided by continuous time segments, so that the accurate attribution of the measurement data can be realized, and the data completeness and clear boundaries in each construction stage are ensured. Then, the hierarchical index mapping structure is established according to the combination of the node index identifier and the measurement data identifier, so that the phased monitoring data subsets can be orderly managed to obtain the phased monitoring data set, forming a clear and systematic construction stage measurement data organization method, and improving the efficiency of subsequent data retrieval and invocation.

[0068] In one exemplary embodiment, the monitoring data of each construction stage in the phased monitoring data set is subjected to curve fitting processing to obtain a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support and protection structure for different construction stages, including steps S401 to S403.

[0069] Step S401, in the phased monitoring data set, the monitoring data corresponding to each physical quantity is subjected to interval resampling processing according to a preset time interval to obtain a phased resampling data set of each physical quantity uniformly distributed in time in each construction stage, and the phased resampling data set includes resampling data of each physical quantity in different construction stages.

[0070] Among them, the phased resampling data set represents the data set obtained by resampling the original phased monitoring data set according to a uniform preset time interval in each construction stage, which is used to ensure the uniformity of the distribution of each physical quantity monitoring data on the time axis.

[0071] Exemplarily, firstly, the time intervals of each physical quantity data collection often have a certain degree of unevenness, such as irregular distribution of data points caused by differences in device sampling rate, data transmission delay or sampling clock error; in order to ensure the stability and consistency of subsequent curve fitting processing, the monitoring data in the stage monitoring data set needs to be resampled according to a unified preset time interval, specifically, within each construction stage, the monitoring data sequence is resampled according to a unified time step, so that the data distribution of each physical quantity on the time axis reaches a state of basic uniformity. Moreover, in the resampling process, for missing time points, the data records are completed by interpolation or reasonable extrapolation to ensure that the time series within the construction stage is continuous and has no obvious discontinuity. Based on this, the stage resampling data set formed after resampling processing covers the resampling data of displacement type monitoring quantities, internal force type monitoring quantities and environmental type monitoring quantities in different construction stages.

[0072] In step S402, the resampling data of each physical quantity in different construction stages in the stage resampling data set is subjected to curve fitting processing respectively, to obtain a stage fitting curve set for displacement type monitoring quantities, internal force type monitoring quantities and environmental type monitoring quantities in different construction stages, and the stage fitting curve set includes stage fitting curves of each physical quantity in different construction stages.

[0073] Among them, the stage fitting curve set represents a group of fitting curves describing the change trend of the physical quantity generated in different construction stages, which is used to express the continuity characteristics of each physical quantity changing with time, such as a trend curve set of displacement changing with time, a trend curve set of internal force changing with time.

[0074] Among them, the displacement type monitoring quantity represents a physical quantity used to measure the spatial displacement change of the supporting and supporting structure during the construction process, which is used to reflect the deformation state of the structure in the horizontal or vertical direction, such as the horizontal displacement of the top of the supporting wall or the vertical settlement of the bottom plate of the foundation pit.

[0075] Among them, the internal force type monitoring quantity represents a physical quantity used to measure the internal force change of the supporting and supporting structure during the construction process, which is used to reflect the change of the tensile, compressive or bending state of the structure, such as the change of the axial force in the steel support member or the change of the bending moment in the supporting pile.

[0076] Among them, the environmental type monitoring quantity represents a physical quantity used to measure the change of external environmental factors affecting the deformation behavior of the foundation pit supporting structure during the construction process, which is used to assist in analyzing the influence of environmental change on the deformation evolution of the structure, such as the change of air temperature, rainfall or groundwater level around the foundation pit.

[0077] Exemplarily, in the phased resampling data set, curve fitting processing needs to be respectively performed on the resampling data of each physical quantity in different construction stages, so as to generate a fitting curve capable of continuously and smoothly describing the change rule of the corresponding physical quantity with time according to the change trend of the resampling data of each physical quantity in the construction stage, wherein: for displacement type monitoring quantity, the change trajectory of the displacement of the support and supporting structure with time needs to be fitted; for internal force type monitoring quantity, the response curve of the internal stress of the support and supporting structure with time needs to be fitted; for environmental monitoring quantity, the dynamic curve of environmental factors such as temperature and humidity with time needs to be fitted. Based on this, after fitting, a set of phased fitting curve sets is obtained for each physical quantity in different construction stages; each phased fitting curve in each set of phased fitting curves represents the dynamic evolution process of a type of physical quantity in a construction stage.

[0078] In step S403, the phased fitting curves corresponding to different physical quantities in the same construction stage are integrated and organized to obtain a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support and supporting structure in different construction stages.

[0079] Exemplarily, the phased fitting curves corresponding to different physical quantities in the same construction stage are integrated and organized, that is, the phased fitting curves respectively fitted for displacement type monitoring quantity, internal force type monitoring quantity and environmental monitoring quantity in different construction stages are aligned on the time axis and organized and archived according to the physical quantity categories, so that each construction stage can reflect the multi-dimensional deformation evolution of the support and supporting structure in the corresponding time period in a unified data structure. Furthermore, in the organization process, the synchronization of data in time between different physical quantities needs to be ensured, so that the change states of each physical quantity can be viewed at the same time node, and the analysis correlation failure caused by the misalignment of data time axis between physical quantities is avoided. Finally, after the above integration and organization processing, a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support and supporting structure in different construction stages is obtained; the dynamic measurement curve set completely reflects the deformation evolution dynamics of the support and supporting structure under the combined action of displacement change, internal force response and environmental change in each construction stage.

[0080] In this embodiment, firstly, the interval resampling processing is performed on the phased monitoring data set to obtain a phased resampling data set, so that the time distribution of each physical quantity in each construction phase is uniform, and the continuous and consistent basis data for subsequent curve fitting is ensured; secondly, the curve fitting processing is performed on the phased resampling data set, so that the phased fitting curve set reflecting the dynamic evolution trend of displacement type monitoring quantity, internal force type monitoring quantity and environmental type monitoring quantity in each construction phase is generated; and thirdly, the phased fitting curves corresponding to different physical quantities in the same construction phase are integrated and organized, so that the dynamic measurement curve set covering each construction phase and associated with multiple physical quantities is formed, and the structured arrangement and continuous dynamic expression of the multiple physical quantity data in the whole construction cycle are realized.

[0081] In one exemplary embodiment, the deformation behavior of the large-area foundation pit support and protection structure in the whole construction cycle is identified according to the dynamic measurement curve set, and a multi-dimensional deformation feature set for describing the overall deformation characteristics and local deformation characteristics of the large-area foundation pit support and protection structure is obtained, including steps S501 to S503.

[0082] Step S501, the whole cycle feature point extraction processing is performed on the dynamic measurement curve set, and a key feature point set covering the deformation process in the whole construction cycle is obtained. The key feature points in the key feature point set include deformation extreme value feature points, deformation rate mutation feature points and deformation trend inflection points.

[0083] Among them, the key feature point set represents a set of several key feature points reflecting the main change characteristics of the deformation process of the large-area foundation pit support and protection structure in the whole construction cycle, which includes deformation extreme value feature points, deformation rate mutation feature points and deformation trend inflection points.

[0084] Among them, the deformation extreme value feature point represents the position point where the displacement or internal force value reaches the maximum or minimum value in the dynamic measurement curve set, which is used to reflect the extreme deformation state of the support and protection structure in the construction process, for example, the time when the horizontal displacement of the foundation pit side wall reaches the maximum value.

[0085] Among them, the deformation rate mutation feature point represents the position point where the deformation rate, i.e. the curve slope, in the dynamic measurement curve set appears obvious mutation, which is used to reflect the time when the deformation speed of the support and protection structure changes dramatically due to the influence of external working conditions, for example, the time when the support axial force suddenly increases due to excavation advancement.

[0086] Among them, the deformation trend inflection point represents the position point where the overall deformation trend in the dynamic measurement curve set changes direction, which is used to reflect the change turning point in the deformation development process of the support and protection structure, for example, the turning point when the horizontal displacement of the foundation pit top changes from growth to convergence.

[0087] Exemplarily, the set of dynamic measurement curves records continuous trajectories of each physical quantity of the large-area foundation pit support and protection structure varying with time in the whole construction cycle, and these trajectories contain complete historical information of deformation evolution of the support and protection structure. Therefore, each measurement curve in the set of dynamic measurement curves needs to be traversed to extract key feature points representing deformation behaviors, i.e., deformation extreme value feature points, deformation rate mutation feature points, and deformation trend inflection points. Among them, the deformation extreme value feature points are used to identify time points of maximum or minimum deformation of the measurement curve in the construction process, reflecting extreme states of the support and protection structure in each construction stage; the deformation rate mutation feature points are used to identify time points of significant change in the slope of the measurement curve, reflecting the phenomenon of sharp change in the deformation rate of the support and protection structure in a short time; and the deformation trend inflection points are used to identify turning positions of overall change trend of the measurement curve from rising to falling or from falling to rising, reflecting change in the deformation development direction in the construction process. Based on this, by systematically extracting these key feature points, key information reflecting essential changes in the deformation behavior of the support and protection structure can be accurately captured without retaining all measurement data, forming a set of key feature points covering the whole construction cycle.

[0088] In step S502, according to the set of key feature points, deformation mode recognition processing is performed on each measurement curve corresponding to different construction stages in the set of dynamic measurement curves, to obtain a set of deformation mode parameters representing overall deformation trend mode and local deformation behavior mode.

[0089] Among them, the set of deformation mode parameters represents a set of various quantitative feature parameters for describing overall deformation trend and local deformation behavior extracted by analyzing the set of dynamic measurement curves according to the key feature points, such as deformation rate change amplitude, extreme value occurrence period, and trend inflection point number.

[0090] Among them, the overall deformation trend mode represents mode features reflecting overall scale deformation change trend of the large-area foundation pit support and protection structure in the whole construction cycle, which are obtained by summarizing the set of deformation mode parameters, and are used to describe deformation direction and change law of the support and protection structure evolving with time.

[0091] Among them, the local deformation behavior mode represents mode features reflecting differential deformation exhibited by local regions or local components of the large-area foundation pit support and protection structure in a specific construction stage, which are obtained by summarizing the set of deformation mode parameters, and are used to identify local abnormal deformation or local specificity change.

[0092] Exemplarily, based on the extracted key feature point set, the deformation mode recognition processing is performed on the measurement curves corresponding to each construction stage in the dynamic measurement curve set, that is, according to the change characteristics of the measurement curves at the key feature point positions, the change law of the measurement curves in different construction stages is summarized. Specifically, based on the key feature points corresponding to the dynamic measurement curve set, the deformation amplitude, deformation rate change trend, inflection point number and distribution characteristics and other indexes of each measurement curve in the construction stage are analyzed, and the characteristic parameters reflecting the overall deformation trend and local deformation behavior are extracted, wherein: the overall deformation trend mode describes the overall deformation direction and change characteristics of the support and protection structure in the whole construction cycle, for example, the overall presents the trend of convergence, expansion or stage shrinkage and expansion alternation; the local deformation behavior mode describes the local abnormal deformation phenomenon appearing at certain time period or certain monitoring point position, for example, local accelerated deformation or local deformation reverse change. Finally, the above various characteristic parameters are systematically classified and arranged to generate a deformation mode parameter set, which summarizes the mode characteristics of the deformation evolution of the support and protection structure in different construction stages and different spatial positions in the form of structured parameters.

[0093] In step S503, based on the similarity between the spatial coordinate data of the multi-class monitoring point group and the deformation mode parameter set, spatial feature aggregation processing is performed on the deformation mode parameter set to obtain a multi-dimensional deformation feature set for comprehensively describing the overall deformation characteristics and local differential deformation characteristics of the large-area foundation pit support and protection structure.

[0094] Among them, the spatial coordinate data of the multi-class monitoring point group represents the specific physical position of the different categories of monitoring points arranged in the large-area foundation pit support and protection structure and the surrounding area in space, for example, the three-dimensional coordinate position corresponding to the monitoring point.

[0095] Exemplarily, based on the deformation mode parameter set, combined with the spatial coordinate data of the multi-class monitoring point group, spatial feature aggregation processing is performed, that is, the parameter data belonging to spatial proximity or similar change characteristics in the deformation mode parameter set is classified and integrated, so as to systematically depict the overall deformation characteristics and local differential deformation characteristics of the support and protection structure. Specifically, first, the physical space position of each monitoring point is determined according to the spatial coordinate data of each monitoring point, and then the deformation mode parameter set is mapped to the physical space position of the corresponding monitoring point; further, the similarity between the parameter data respectively mapped to adjacent monitoring points is analyzed, including deformation trend consistency, extreme value position proximity, rate change synchronization and other characteristics, and grouping and classification are performed according to the preset similarity standard.

[0096] Based on this, the deformation mode parameter set mapped to the corresponding physical space position can be divided into data contained in several space regions with similar deformation behavior characteristics, and the data aggregated in each space region shows consistent or highly correlated deformation characteristics. Finally, based on these space aggregation results, a multi-dimensional deformation feature set is formed for comprehensively describing the overall deformation characteristics and local differential deformation characteristics of the large-area foundation pit support and protection structure.

[0097] In this embodiment, first, full-cycle feature point extraction processing is performed on the dynamic measurement curve set, so that a key feature point set reflecting key deformation characteristics in the whole construction cycle can be extracted, and the simplicity and representativeness of the deformation process representation are improved. Further, according to the key feature point set, deformation mode recognition processing is performed on each measurement curve corresponding to different construction stages in the dynamic measurement curve set, so that the overall deformation trend mode and the local deformation behavior mode can be summarized, and a deformation mode parameter set is obtained, which systematically describes the deformation law of the support and protection structure in different stages and different regions. Further, spatial feature aggregation processing is performed according to the similarity between the spatial coordinate data of the multi-class monitoring point group and the deformation mode parameter set, so that a multi-dimensional deformation feature set describing the overall and local multi-dimensional deformation characteristics can be comprehensively obtained, so that the system identification and multi-dimensional feature construction of the deformation evolution mode of the large-area foundation pit support and protection structure in the whole construction cycle can be realized.

[0098] In one exemplary embodiment, according to the key feature point set, deformation mode recognition processing is performed on each measurement curve corresponding to different construction stages in the dynamic measurement curve set, and a deformation mode parameter set representing the overall deformation trend mode and the local deformation behavior mode is obtained, including steps S601 to S602.

[0099] Step S601, according to the deformation main direction, the main deformation rate and the overall deformation amplitude reflected by the key feature points in the key feature point set, global feature extraction processing is performed on each measurement curve corresponding to different construction stages, and a deformation mode parameter set representing the overall deformation trend mode is obtained.

[0100] The deformation main direction represents the main spatial direction of the overall deformation change trend of the support and protection structure in the construction stage, that is, it is used to reflect the dominant trend of the overall deformation of the support and protection structure, for example, the direction of the overall movement of the support wall into the foundation pit.

[0101] The main deformation rate represents the main deformation development speed level of the support and protection structure in the construction stage in the time evolution process, that is, it is used to describe the speed of the deformation process, for example, the rate of horizontal displacement growth per unit time of the support member after the support is removed.

[0102] The overall deformation range represents a range between a maximum deformation and a minimum deformation of the support and protection structure in the construction process, i.e., is used to quantify the deformation of the support and protection structure under the overall stress response, for example, a range of horizontal displacement of the top of the foundation pit slope in a stage.

[0103] Exemplarily, according to the various types of key feature points extracted from the key feature point set, global feature extraction processing is performed on each measurement curve corresponding to different construction stages in the dynamic measurement curve set, i.e., each measurement curve records the deformation process of the support and protection structure in a specific construction stage. Therefore, during global feature extraction, systematic analysis is performed around the global analysis related feature indexes such as the deformation main direction, the main deformation rate, and the overall deformation range represented by the key feature points. Specifically, the deformation main direction is used to quantify the main deformation trend direction of the support and protection structure in the corresponding construction stage, for example, a contraction trend toward the center of the foundation pit or an expansion trend away from the center of the foundation pit; the main deformation rate is used to quantify the overall deformation speed level of the support and protection structure in the corresponding construction stage, reflecting the size and stability of the deformation development rate; and the overall deformation range is used to quantify the maximum deformation degree of the support and protection structure in the entire construction stage.

[0104] Based on the feature indexes reflected by the key feature points, the overall analysis is performed on each measurement curve to extract the numerical features corresponding to the feature indexes, forming a deformation mode parameter set representing the overall deformation trend mode.

[0105] In step S602, according to the change rate abnormality degree and the trend turning frequency reflected by the key feature points in the key feature point set, local feature extraction processing is performed on each measurement curve corresponding to different construction stages, to obtain a deformation mode parameter set representing a local deformation behavior mode.

[0106] The change rate abnormality degree represents an abnormal fluctuation degree of the deformation rate of the support and protection structure relative to the average rate in the construction stage, which is used to identify the violent deformation behavior of the support and protection structure caused by stress or working condition changes in a short time, for example, a case where the horizontal displacement rate of the local area support wall significantly increases due to excavation operation.

[0107] The trend turning frequency represents the number of times of change in the deformation trend direction of the support and protection structure in the construction stage, which is used to reflect the complexity of the deformation trend change of the support and protection structure under the action of multiple disturbance factors in the construction process, for example, the number of times of rising and falling turning of the deformation curve of the support and protection structure under the alternating action of support construction and dewatering operation.

[0108] Exemplarily, after the global feature extraction is completed, further local feature extraction processing is performed on each measurement curve in the dynamic measurement curve set of each construction stage according to the local analysis related feature indexes extracted from the key feature point set, that is, systematic analysis is performed around the local analysis related feature indexes such as the change rate abnormality degree and the trend turning frequency represented by the key feature points. Specifically, the change rate abnormality degree is used to evaluate the severity of the deformation rate change amplitude of the measurement curve in different time periods, so as to quantitatively describe the condition of sudden change of deformation rate in a short time; the trend turning frequency is used to evaluate the number of trend changes of the measurement curve in the whole construction cycle, for example, the frequency of inflection points where the displacement growth trend changes to a downward trend or the downward trend changes to a growth trend, so as to quantitatively describe the condition that the supporting and supporting structure in the local area or local time period is affected by multiple disturbances or unstable factors.

[0109] Based on the feature indexes reflected by the key feature points, the whole analysis is performed on each measurement curve, the numerical features corresponding to the feature indexes are extracted, and the deformation mode parameter set representing the local deformation behavior mode is formed.

[0110] In this embodiment, first, the global feature extraction processing is performed on the deformation main direction, the main deformation rate and the overall deformation amplitude reflected by the key feature points, so that the quantitative parameters of the overall deformation trend mode of the supporting and supporting structure in the construction stage can be systematically extracted, and the representation ability of the overall deformation characteristics is improved; secondly, the local feature extraction processing is performed on the change rate abnormality degree and the trend turning frequency reflected by the key feature points, so that the quantitative parameters of the local deformation behavior mode of the supporting and supporting structure in the construction stage can be systematically extracted; based on this, the systematic identification and parameterized modeling of the overall and local deformation characteristics of the supporting and supporting structure in the whole construction cycle are realized.

[0111] In one exemplary embodiment, based on the similarity between the spatial coordinate data of the multi-class monitoring point group and the deformation mode parameter set, spatial feature aggregation processing is performed on the deformation mode parameter set to obtain a multi-dimensional deformation feature set for comprehensively describing the overall deformation characteristics and local differential deformation characteristics of the large-area foundation pit supporting and supporting structure, including steps S701 to S703.

[0112] Step S701, according to the spatial coordinate data of the multi-class monitoring point group, the spatial straight line distance between the monitoring points of the multi-class monitoring point group is calculated to obtain a spatial distance matrix reflecting the spatial distribution relationship of each monitoring point.

[0113] Among them, the spatial distance matrix represents a matrix with monitoring point pairs as indexes and spatial distances as elements, which is used to reflect the distribution relationship of the monitoring point group in the physical space. For example, the horizontal straight-line distance between two displacement monitoring points is 10 meters, and this distance is recorded as an element in the spatial distance matrix.

[0114] For example, first, based on the spatial coordinate data of each monitoring point in a multi-class monitoring point group, a spatial relationship analysis is performed between each monitoring point. That is, based on the principles of spatial geometry, by extracting the difference in the coordinate components of any two monitoring points, the actual spatial distance value between the two monitoring points is obtained using the Euclidean distance formula or other standard distance calculation methods. Furthermore, by performing pairwise distance calculations on all pairs of monitoring points in the multi-class monitoring point group, a complete spatial distance matrix can be formed, in which each element in the spatial distance matrix corresponds to the spatial distance value between a pair of monitoring points. Based on this, the spatial distance matrix can comprehensively reflect the spatial distribution relationship of each monitoring point in the multi-class monitoring point group, including dense and sparse areas of monitoring points, as well as local abnormal distribution conditions.

[0115] In step S702 , similarity matching is performed on the spatial distance matrix and the deformation pattern parameter set according to a weighted fusion method combining the spatial distance and the pattern feature distance to obtain a similarity scoring matrix.

[0116] Among them, the weighted fusion method of combining spatial distance and pattern feature distance means that after normalizing the spatial distance and deformation pattern feature distance between monitoring points respectively, they are weighted summed according to the set weight coefficient, thereby comprehensively reflecting the comprehensive similarity of the monitoring points in terms of spatial position proximity and deformation feature similarity, which is used to construct a unified dimensional similarity evaluation basis.

[0117] Among them, the similarity score matrix represents a matrix calculated based on a weighted fusion method combining spatial distance and pattern feature distance. Each element in the matrix represents the comprehensive similarity score of a pair of monitoring points in spatial distribution and deformation characteristics. For example, the higher the score, the more similar the two monitoring points are in spatial distribution and deformation characteristics.

[0118] Among them, the representation of the pattern characteristic distance is based on the calculation results of the numerical differences between the characteristic parameters extracted from the deformation pattern parameter set of different monitoring points, which is used to quantify the degree of characteristic similarity between different monitoring points in terms of the overall deformation trend pattern and the local deformation behavior pattern. For example, the smaller the numerical difference between the two monitoring points in characteristic indicators such as the main deformation direction, main deformation rate and change rate anomaly, the smaller their pattern characteristic distance is, and the greater the degree of characteristic similarity between the overall deformation trend pattern and the local deformation behavior pattern.

[0119] Exemplarily, firstly, on the basis of the spatial distance matrix obtained, the spatial distance matrix needs to be further fused with the deformation mode parameter set for similarity matching calculation; in order to make the matching process reflect both the proximity relationship in space and the similarity in deformation characteristics, a weighted fusion method combining spatial distance and mode feature distance needs to be adopted. Further, according to the overall deformation trend mode and the local deformation behavior mode characteristics corresponding to each monitoring point in the deformation mode parameter set, the difference degree between different monitoring points in the feature space is calculated to form a mode feature distance matrix; each element in the mode feature distance matrix corresponds to the feature similarity degree between a pair of monitoring points.

[0120] Then, by setting reasonable weighting coefficients, the spatial distance matrix and the mode feature distance matrix are weighted and fused to obtain a similarity score matrix that comprehensively reflects the dual similarity relationship of space and features; in the process of weighted fusion, the spatial distance and the mode feature distance need to be normalized to eliminate the influence of dimensional differences on the similarity score and ensure the comparability and consistency of the fusion result. Based on this, each element in the finally formed similarity score matrix reflects the comprehensive similarity degree of a pair of monitoring points in terms of spatial distribution and deformation characteristics; by constructing such a similarity score matrix, a quantitative basis can be provided for subsequent clustering analysis based on comprehensive judgment of spatial distribution and deformation characteristics, so that the final clustering result can take into account both the physical distribution rationality and the deformation characteristic homogeneity.

[0121] Step S703, based on the preset density peak clustering algorithm, the similarity score matrix is subjected to spatial clustering processing to obtain a multi-dimensional deformation feature set for comprehensively describing the overall deformation characteristics and the local differential deformation characteristics of the large-area foundation pit support and protection structure.

[0122] Among them, the density peak clustering algorithm represents a method of identifying data with higher local density and greater distance from other density peak points as clustering centers, and expanding multiple clustering clusters with the clustering centers as the core, for grouping and classifying feature parameter data with similar deformation characteristics and spatial proximity.

[0123] For example, first, the specific location of each monitoring point in physical space must be determined based on the spatial coordinate data of each monitoring point, and this serves as the spatial reference basis for subsequent clustering processing. Subsequently, the characteristic parameter data corresponding to each monitoring point in the deformation pattern parameter set is accurately mapped to the location of the monitoring point in physical space, establishing a one-to-one correspondence between deformation characteristics and spatial positions. On this basis, instead of directly clustering the monitoring points themselves, in-depth analysis and classification are performed on the characteristic parameter data mapped to the spatial positions. Specifically, the similarity of the characteristic parameter data between adjacent monitoring points needs to be analyzed. The similarity analysis content may include: consistency of deformation trends, that is, the degree of consistency of the main deformation direction and main deformation rate of each monitoring point; proximity of extreme value positions, that is, the consistency of the time period when the maximum or minimum deformation occurs; and synchronization of deformation rate changes, that is, the degree of coordination of rate changes during the deformation development process.

[0124] By comprehensively judging the similarities of the above-mentioned multiple feature dimensions and combining the linear distance relationship between each monitoring point in physical space, grouping and classification processing are performed according to the preset similarity standard. In the specific clustering process, a preset density peak clustering algorithm is used to determine the local high similarity and high density of monitoring points under the dual constraints of spatial distance and pattern feature distance, forming clusters that are spatially continuous and have consistent deformation characteristics. The characteristic parameter data within each cluster exhibits highly consistent overall and local deformation characteristics, while the characteristic parameter data between different clusters have significant differences in deformation characteristics or spatial location. Finally, based on the deformation pattern characteristics represented by each cluster, a multidimensional deformation feature set is systematically summarized to comprehensively describe the overall deformation characteristics and local differential deformation characteristics of large-scale foundation pit support and retaining structures throughout the construction cycle.

[0125] In this embodiment, first, the spatial coordinate data of multiple types of monitoring point groups are calculated and processed according to the spatial distance matrix, so as to accurately reflect the physical spatial distribution relationship between each monitoring point; secondly, according to the weighted fusion method combining spatial distance and pattern feature distance, the spatial distance matrix and the deformation pattern parameter set are similarity matched to obtain a similarity scoring matrix, so as to comprehensively characterize the degree of correlation between the monitoring points in spatial position and deformation characteristics; thirdly, the similarity scoring matrix is ​​spatially clustered according to the density peak clustering algorithm, so as to realize the orderly grouping and classification of the characteristic parameter data in the deformation pattern parameter set, extract the overall and local deformation characteristics to obtain a multi-dimensional deformation feature set, and realize the spatial correlation identification and systematic modeling of the overall and local deformation characteristics of the large-area foundation pit support and retaining structure.

[0126] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least some of the other steps or steps or stages in other steps.

[0127] Based on the same inventive concept, the embodiments of the present application also provide a large-area foundation pit support structure deformation automatic measurement system for implementing the large-area foundation pit support structure deformation automatic measurement method described above. The problem-solving implementation scheme provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more large-area foundation pit support structure deformation automatic measurement system embodiments provided below can refer to the limitations of the large-area foundation pit support structure deformation automatic measurement method described above, and will not be repeated here.

[0128] In one exemplary embodiment, as shown in Figure 2 a large-area foundation pit support structure deformation automatic measurement system is provided, comprising: an acquisition module 201, a curve fitting module 202, and a pattern recognition module 203, wherein: The acquisition module 201 is configured to perform synchronous data acquisition and processing on a plurality of monitoring point groups arranged according to the large-area foundation pit support structure, to obtain an initial measurement data set for characterizing the deformation state of the large-area foundation pit support structure, wherein the plurality of monitoring point groups include displacement monitoring points, internal force monitoring points, and environmental monitoring points. The curve fitting module 202 is configured to perform stage grouping processing on the initial measurement data set based on a time sequence division manner indexed by construction condition change nodes, to obtain a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support structure for different construction stages. The pattern recognition module 203 is configured to perform pattern recognition processing on the deformation behavior of the large-area foundation pit support structure during the entire construction cycle according to the dynamic measurement curve set, to obtain a multi-dimensional deformation feature set for describing the overall deformation characteristics and local deformation characteristics of the large-area foundation pit support structure, and the multi-dimensional deformation feature set is used to implement stage evaluation, abnormal trend early warning, and construction control decision of the deformation state of the large-area foundation pit support structure.

[0129] In an example embodiment, the curve fitting module 202 is further configured to: perform node identification processing on the initial measurement data set according to construction condition change nodes reflected by respective construction condition change time points recorded in the construction log, to obtain a node index set corresponding to the large-area foundation pit support and protection structure excavation completion node, the support removal node, and the support replacement completion node; perform stage interval division processing on the initial measurement data set at the continuous construction condition change nodes according to the node index set, to obtain a stage monitoring data set bounded by adjacent construction condition change nodes, the stage monitoring data set including monitoring data of each construction stage; and perform curve fitting processing on the monitoring data of each construction stage in the stage monitoring data set, to obtain a dynamic measurement curve set of the large-area foundation pit support and protection structure deformation evolution process for different construction stages.

[0130] In an example embodiment, the curve fitting module 202 is further configured to: perform sequential arrangement processing on the node index set according to time stamp fields corresponding to respective construction condition change nodes, to obtain an ordered time sequence set corresponding to the continuous construction condition change nodes, the ordered time sequence set including node index identifiers corresponding to continuous time sections; match respective measurement data in the initial measurement data set to node index identifiers of corresponding time sections in the ordered time sequence set according to time labels of the respective measurement data in the initial measurement data set, to obtain respective stage monitoring data sub-sets divided according to the continuous time sections; and perform composite index processing on the respective stage monitoring data sub-sets according to a hierarchical index mapping structure established based on key-value pairs of the node index identifiers and the measurement data identifiers, to obtain the stage monitoring data set bounded by adjacent construction condition change nodes.

[0131] In an example embodiment, the curve fitting module 202 is further configured to: perform interval sampling processing on the monitoring data corresponding to respective physical quantities in the stage monitoring data set according to a preset time interval, to obtain a stage resampling data set in which respective physical quantities are uniformly distributed in time within respective construction stages, the stage resampling data set including resampling data of respective physical quantities in different construction stages; perform curve fitting processing on the resampling data of respective physical quantities in different construction stages in the stage resampling data set, respectively, to obtain a stage fitting curve set of displacement type monitoring quantities, internal force type monitoring quantities, and environmental type monitoring quantities in different construction stages, respectively, the stage fitting curve set including stage fitting curves of respective physical quantities in different construction stages; and perform integration and organization processing on the stage fitting curves corresponding to different physical quantities within the same construction stage, to obtain the dynamic measurement curve set of the large-area foundation pit support and protection structure deformation evolution process for different construction stages.

[0132] In an example embodiment, the pattern recognition module 203 is further configured to: perform full-cycle feature point extraction processing on the set of dynamic measurement curves to obtain a set of key feature points covering the deformation process in the whole construction cycle, the key feature points in the set of key feature points including deformation extreme value feature points, deformation rate mutation feature points, and deformation trend inflection points; perform deformation pattern recognition processing on each measurement curve corresponding to different construction stages in the set of dynamic measurement curves according to the set of key feature points to obtain a set of deformation pattern parameters representing the overall deformation trend pattern and the local deformation behavior pattern; and perform spatial feature aggregation processing on the set of deformation pattern parameters based on the similarity between the spatial coordinate data of the multi-class monitoring point group and the set of deformation pattern parameters to obtain a multi-dimensional deformation feature set for comprehensively describing the overall deformation characteristics and the local differential deformation characteristics of the large-area foundation pit support and protection structure.

[0133] In an example embodiment, the pattern recognition module 203 is further configured to: perform global feature extraction processing on each measurement curve corresponding to different construction stages according to the deformation main direction, the main deformation rate, and the overall deformation amplitude reflected by the key feature points in the set of key feature points to obtain a set of deformation pattern parameters representing the overall deformation trend pattern; and perform local feature extraction processing on each measurement curve corresponding to different construction stages according to the change rate abnormality and the trend turning frequency reflected by the key feature points in the set of key feature points to obtain a set of deformation pattern parameters representing the local deformation behavior pattern.

[0134] In an example embodiment, the pattern recognition module 203 is further configured to: calculate the spatial straight-line distance between monitoring points in the multi-class monitoring point group according to the spatial coordinate data of the multi-class monitoring point group to obtain a spatial distance matrix reflecting the spatial distribution relationship of each monitoring point; perform similarity matching processing on the spatial distance matrix and the set of deformation pattern parameters according to a weighted fusion mode combining the spatial distance and the pattern feature distance to obtain a similarity score matrix; and perform spatial clustering processing on the similarity score matrix based on a preset density peak clustering algorithm to obtain a multi-dimensional deformation feature set for comprehensively describing the overall deformation characteristics and the local differential deformation characteristics of the large-area foundation pit support and protection structure.

[0135] The above-mentioned modules in the automatic measurement system for deformation of a large-area foundation pit support and protection structure can be realized by software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned modules.

[0136] In an example embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of any of the above embodiments when executing the computer program.

[0137] In an example embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps of any of the above embodiments when executed by a processor.

[0138] A person of ordinary skill in the art can understand that all or part of the processes in the above embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above embodiments. Any reference to a memory, database, or other medium in the embodiments provided by the present application can include at least one of a non-volatile and volatile memory. The non-volatile memory can include a read-only memory (ROM), a tape, a floppy disk, a flash memory, an optical storage, a high-density embedded non-volatile memory, a resistive memory (ReRAM), a magnetoresistive random access memory (MRAM), a ferroelectric memory (FRAM), a phase change memory (PCM), a graphene memory, etc. The volatile memory can include a random access memory (RAM) or an external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., and is not limited thereto. The processor involved in the embodiments provided by the present application can be a general processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0139] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not contradict, they should be considered as within the scope of the present application.

[0140] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific manner, but should not be construed as limiting the scope of the patent of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for automatic measurement of deformation of a large-area foundation pit support and protection structure, characterized in that, The method comprises: Synchronous data acquisition and processing is performed on a plurality of monitoring point groups arranged according to a large-area foundation pit support and protection structure, to obtain an initial measurement data set for representing a deformation state of the large-area foundation pit support and protection structure, wherein the plurality of monitoring point groups comprise displacement monitoring points, internal force monitoring points and environmental monitoring points; Based on a time sequence division mode with construction condition change nodes as indexes, stage grouping processing is performed on the initial measurement data set, to obtain a dynamic measurement curve set of a deformation evolution process of the large-area foundation pit support and protection structure in different construction stages; According to the dynamic measurement curve set, mode recognition processing is performed on a deformation behavior of the large-area foundation pit support and protection structure in a whole construction cycle, to obtain a multi-dimensional deformation feature set for describing overall deformation characteristics and local deformation characteristics of the large-area foundation pit support and protection structure, and the multi-dimensional deformation feature set is used to realize stage evaluation, abnormal trend early warning and construction control decision of the deformation state of the large-area foundation pit support and protection structure.

2. The method of claim 1, wherein, The stage grouping processing on the initial measurement data set based on the time sequence division mode with construction condition change nodes as indexes to obtain the dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support and protection structure in different construction stages comprises: According to construction condition change nodes reflected by each construction condition change time recorded in a construction log, node identification processing is performed on the initial measurement data set, to obtain a node index set corresponding to a large-area foundation pit support and protection structure excavation completion node, a support removal node and a support replacement completion node; According to the node index set, stage interval division processing is performed on the initial measurement data set at continuous construction condition change nodes, to obtain stage monitoring data sets bounded by adjacent construction condition change nodes, wherein the stage monitoring data sets comprise monitoring data of each construction stage; Curve fitting processing is performed on the monitoring data of each construction stage in the stage monitoring data sets, to obtain the dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support and protection structure in different construction stages.

3. The method of claim 2, wherein, The stage interval division processing on the initial measurement data set at continuous construction condition change nodes according to the node index set to obtain the stage monitoring data sets bounded by adjacent construction condition change nodes comprises: According to time stamp fields corresponding to each construction condition change node, sequential arrangement processing is performed on the node index set, to obtain an ordered time sequence set corresponding to continuous construction condition change nodes, wherein the ordered time sequence set comprises node index identifications corresponding to continuous time sections; According to time labels of each measurement data in the initial measurement data set, each measurement data in the initial measurement data set is matched to a node index identification of a corresponding time section in the ordered time sequence set, to obtain each stage monitoring data sub-set divided according to continuous time sections; According to the hierarchical index mapping structure established based on the key-value pairs of the node index identifier and the measurement data identifier, the various stage monitoring data subsets are subjected to composite index processing to obtain a stage monitoring data set bounded by adjacent construction condition change nodes.

4. The method of claim 2, wherein, The monitoring data of each construction stage in the stage monitoring data set is subjected to curve fitting processing to obtain a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support and protection structure for different construction stages, including: In the stage monitoring data set, the monitoring data corresponding to each physical quantity is subjected to interval resampling processing according to a preset time interval to obtain a stage resampling data set of each physical quantity uniformly distributed in time within each construction stage, and the stage resampling data set includes resampling data of each physical quantity in different construction stages; In the stage resampling data set, the resampling data of each physical quantity in different construction stages is subjected to curve fitting processing respectively to obtain a stage fitting curve set of the displacement type monitoring quantity, the internal force type monitoring quantity and the environmental type monitoring quantity respectively in different construction stages, and the stage fitting curve set includes stage fitting curves of each physical quantity in different construction stages; The stage fitting curves corresponding to different physical quantities in the same construction stage are subjected to integration and organization processing to obtain a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support and protection structure for different construction stages.

5. The method of claim 1, wherein, According to the dynamic measurement curve set, the deformation behavior of the large-area foundation pit support and protection structure in the whole construction cycle is subjected to pattern recognition processing to obtain a multi-dimensional deformation feature set for describing the overall deformation characteristics and the local deformation characteristics of the large-area foundation pit support and protection structure, including: The dynamic measurement curve set is subjected to whole-cycle feature point extraction processing to obtain a key feature point set covering the deformation process in the whole construction cycle, and the key feature points in the key feature point set include deformation extreme value feature points, deformation rate mutation feature points and deformation trend inflection points; According to the key feature point set, each measurement curve corresponding to different construction stages in the dynamic measurement curve set is subjected to deformation pattern recognition processing to obtain a deformation mode parameter set representing the overall deformation trend mode and the local deformation behavior mode; Based on the similarity between the spatial coordinate data of the multi-class monitoring point group and the deformation mode parameter set, the deformation mode parameter set is subjected to spatial feature aggregation processing to obtain a multi-dimensional deformation feature set for comprehensively describing the overall deformation characteristics and the local differential deformation characteristics of the large-area foundation pit support and protection structure.

6. The method of claim 5, wherein, According to the key feature point set, each measurement curve corresponding to different construction stages in the dynamic measurement curve set is subjected to deformation pattern recognition processing to obtain a deformation mode parameter set representing the overall deformation trend mode and the local deformation behavior mode, including: According to the main deformation direction, the main deformation rate and the overall deformation amplitude reflected by the key feature points in the key feature point set, global feature extraction processing is performed on each measurement curve corresponding to different construction stages to obtain a deformation mode parameter set representing the overall deformation trend mode; According to the change rate anomaly degree and the trend turning frequency reflected by the key feature points in the key feature point set, local feature extraction processing is performed on each measurement curve corresponding to different construction stages to obtain a deformation mode parameter set representing the local deformation behavior mode.

7. The method of claim 5, wherein, The similarity between the spatial coordinate data of the multi-class monitoring point group and the deformation mode parameter set is used for spatial feature aggregation processing of the deformation mode parameter set to obtain a multi-dimensional deformation feature set for comprehensively describing the overall deformation characteristics and the local differential deformation characteristics of the large-area foundation pit support and protection structure, including: According to the spatial coordinate data of the multi-class monitoring point group, the spatial straight-line distance between monitoring points of the multi-class monitoring point group is calculated to obtain a spatial distance matrix reflecting the spatial distribution relationship of each monitoring point; According to a weighted fusion mode combining spatial distance and mode feature distance, similarity matching processing is performed on the spatial distance matrix and the deformation mode parameter set to obtain a similarity score matrix; Based on a preset density peak value clustering algorithm, spatial clustering processing is performed on the similarity score matrix to obtain a multi-dimensional deformation feature set for comprehensively describing the overall deformation characteristics and the local differential deformation characteristics of the large-area foundation pit support and protection structure.

8. A system for automatic measurement of deformation of a large area foundation pit support and protection structure, characterized in that, The system comprises: A collection module is configured to perform synchronous data collection processing on a multi-class monitoring point group arranged according to the large-area foundation pit support and protection structure to obtain an initial measurement data set for representing the deformation state of the large-area foundation pit support and protection structure, wherein the multi-class monitoring point group includes displacement monitoring points, internal force monitoring points and environmental monitoring points; A curve fitting module is configured to perform stage grouping processing on the initial measurement data set based on a time sequence division mode with construction condition change nodes as indexes to obtain a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support and protection structure for different construction stages; A mode recognition module is configured to perform mode recognition processing on the deformation behavior of the large-area foundation pit support and protection structure in the whole construction cycle according to the dynamic measurement curve set to obtain a multi-dimensional deformation feature set for describing the overall deformation characteristics and the local deformation characteristics of the large-area foundation pit support and protection structure, wherein the multi-dimensional deformation feature set is used to realize stage evaluation, abnormal trend early warning and construction control decision of the deformation state of the large-area foundation pit support and protection structure. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method of any one of claims 1 to 7.

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