Automatic measurement method and system for deformation of large-area foundation pit support and protection structure

Abstract

The application relates to a large-area foundation pit support and protection structure deformation automatic measurement method and system. The method comprises the following steps: 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 a support and protection structure deformation state; based on a time sequence division mode with a construction condition change node as an index, performing stage grouping processing on the initial measurement data set, to obtain a dynamic measurement curve set of a support and protection structure deformation evolution process for different construction stages; and performing mode recognition processing on a deformation behavior of the support and protection structure in a whole construction cycle according to the dynamic measurement curve set, to obtain a multi-dimensional deformation feature set for describing overall deformation characteristics and local deformation characteristics of the support and protection structure. The method can improve accurate identification and reliable analysis of large-area foundation pit support and protection structure deformation rules and abnormal deformation phenomena.

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:

[0006] 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;

[0007] based on a time series division method with construction condition change nodes as indexes, performing stage grouping processing on the initial measurement data set to obtain a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support structure for different construction stages;

[0008] 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, 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 structure.

[0009] In a second aspect, the application further provides an automatic measurement system for deformation of a large-area foundation pit support structure, comprising:

[0010] a collection module configured to perform synchronous data collection and processing on a plurality of monitoring point groups arranged on the large-area foundation pit support structure, to obtain an initial measurement data set for representing a deformation state of the large-area foundation pit support structure, wherein the plurality of monitoring point groups comprise displacement monitoring points, internal force monitoring points and environmental monitoring points;

[0011] a curve fitting module configured to perform stage grouping processing on the initial measurement data set based on a time sequence division manner with construction condition change nodes as indexes, to obtain a dynamic measurement curve set of a deformation evolution process of the large-area foundation pit support structure for different construction stages;

[0012] a pattern recognition module configured to perform pattern recognition processing on deformation behaviors of the large-area foundation pit support structure in a whole construction cycle according to the dynamic measurement curve set, to obtain a multi-dimensional deformation feature set for describing 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 realize stage evaluation, abnormal trend early warning and construction control decision of the deformation state of the large-area foundation pit support structure.

[0013] In a third aspect, the application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor realizes the above steps when executing the computer program.

[0014] In a fourth aspect, the 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.

[0015] The automatic measurement method, system, computer device and computer readable storage medium for deformation of a large-area foundation pit support and protection structure can first perform synchronous data acquisition and processing on multiple types of monitoring point groups to obtain an initial measurement data set representing the deformation state of the large-area foundation pit support and protection structure, thereby ensuring the spatiotemporal consistency and integrity of the measurement data of the monitoring points; secondly, the initial measurement data set is grouped and processed in stages according to a time series division mode with construction condition change nodes as indexes to obtain a dynamic measurement curve set, thereby revealing the deformation change process of the support and protection structure with the construction progress in stages; thirdly, the dynamic measurement curve set is subjected to pattern recognition processing to obtain a multi-dimensional deformation feature set for describing the overall deformation characteristics and local deformation characteristics, thereby comprehensively describing the deformation behavior of the support and protection structure in the entire 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 of the deformation state of the large-area foundation pit support and protection structure, abnormal trend early warning and construction control decision-making. BRIEF DESCRIPTION OF DRAWINGS

[0016] 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 descriptions. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0017] Figure 1 A flowchart of an automatic measurement method for deformation of a large-area foundation pit support and protection structure in an embodiment;

[0018] Figure 2 A structural block diagram of an automatic measurement system for deformation of a large-area foundation pit support and protection structure in an embodiment. DETAILED DESCRIPTION

[0019] 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.

[0020] In one embodiment, as shown in Figure 1 An automatic measurement method for 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 realized through the interaction of the terminal and the server. In this embodiment, the method includes the following steps S101 to S103.

[0021] Step S101, synchronously collecting and processing the data of the multiple types 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, the multiple types of monitoring point groups including displacement type monitoring points, internal force type monitoring points and environment type monitoring points.

[0022] Wherein, the large-area foundation pit support and protection structure represents a large-scale support and reinforcement system arranged during the underground space excavation construction process to prevent soil collapse, foundation pit deformation or damage to the surrounding environment, for maintaining the stability of the foundation pit enclosure system during excavation, such as the large-scale internal support system arranged in deep foundation pit engineering, including continuous wall, steel support, pile-anchor system and other structures.

[0023] Wherein, the multiple types of monitoring point groups represent a set of multiple monitoring points 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 type monitoring points, internal force type monitoring points and environment type monitoring points.

[0024] Wherein, the displacement type monitoring point represents a monitoring unit specially used for measuring the spatial displacement change of the support and protection structure or the surrounding soil during excavation, to obtain the deformation amount, deformation direction and other data of the support and protection structure, such as displacement sensors or inclinometers installed on support beam nodes or foundation pit slope surfaces.

[0025] Wherein, the internal force type monitoring point represents a monitoring unit for measuring the force change inside the support and protection structure or inside the support member, to obtain the force state of the support and protection structure at different construction stages, such as stress gauges installed on steel supports to measure axial force changes or strain gauges embedded in support pile bodies.

[0026] Wherein, the environment type monitoring point represents a monitoring unit for monitoring the change of external natural environment parameters affecting the deformation behavior of the support and protection structure, to obtain environmental data such as temperature, humidity, rainfall, groundwater level, etc., such as automatic weather stations arranged on the top of the foundation pit slope or groundwater level monitoring wells arranged around the foundation pit.

[0027] Wherein, the initial measurement data set represents the original data set that completely describes the deformation state of the large-area foundation pit support and protection structure after the measurement data of the multiple types of monitoring points are synchronously collected and processed in a unified format.

[0028] Exemplarily, first, a plurality of monitoring points including displacement type monitoring points, internal force type monitoring points and environmental type monitoring points are set in advance for a large-area foundation pit support and protection structure, wherein the displacement type monitoring points are used to detect the deformation amount of the support and protection structure at different time periods and different spatial positions, the internal force type monitoring points are used to detect the stress change borne by the support and protection structure inside, 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 and protection structure. Further, the plurality of monitoring points are synchronously collected and processed to ensure that the data has time consistency and spatial correlation, that is, at a unified sampling time, the numerical information of each type of monitoring point in the current state is collected according to a unified sampling frequency.

[0029] Further, after the collection is completed, all collected data needs to be preliminarily arranged, including data format standardization, invalid value elimination, sampling time stamp alignment and the like, so as to form an initial measurement data set with clear structure and coherent time sequence, which completely covers the quantification representation of displacement level, internal force level and environmental level at each monitoring point and each time node.

[0030] Step S102, based on a time sequence division mode with construction condition change nodes as indexes, the initial measurement data set is processed in a phased grouping manner 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.

[0031] 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 key nodes of support beam installation completion, foundation pit excavation reaching a certain design depth, enclosure wall grouting reinforcement completion and the like.

[0032] Among them, the construction stage represents a work stage unit with relatively unified construction activity characteristics in a specific time period divided by the construction condition change node as a boundary, 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 a construction stage from the initial excavation of the foundation pit to the completion of the installation of the first support, and another construction stage from the completion of the installation of the first support to the completion of the second excavation.

[0033] Among them, the dynamic measurement curve set represents a dynamic evolution track for exhibiting the displacement, internal force and environmental parameters of the large-area foundation pit support and protection structure changing with time in each construction stage, such as a stress change curve set in which the support axial force gradually increases with the increase of the excavation depth, or a deformation curve set in which the horizontal displacement of the top of the foundation pit slope gradually increases with time.

[0034] Exemplarily, first, the actual construction condition change nodes in the construction process are taken as time indexes, and the initial measurement data set is node processed on the time axis, that is, the corresponding data indexes are marked at the positions of the construction condition change nodes. Subsequently, the continuous time intervals are demarcated according to the construction condition change nodes, and the initial measurement data set is divided into a plurality of data sub-sets with clear time boundaries, each data sub-set corresponding to the measurement data records in a construction stage; this division manner can make the measurement data records in different construction stages independent of each other, facilitating the separate analysis of the change of the deformation evolution of the support and protection structure in each construction stage. Further, after the data segmentation, the data sub-set in each construction stage 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 manner, 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 stages can be clearly presented.

[0035] In step S103, the deformation behavior of the large-area foundation pit support and protection structure in the whole construction period is processed 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 the stage evaluation of the deformation state of the large-area foundation pit support and protection structure, the abnormal trend early warning and the construction control decision.

[0036] Wherein, the whole construction period represents the complete time range experienced during the period from the start of the construction activity of the large-area foundation pit support and protection structure to the completion of all construction tasks, which is used to comprehensively cover the change process of the deformation behavior of the support and protection structure in each construction stage, such as all construction time periods from the start of the first excavation of the foundation pit to the end of the removal of the last layer of support.

[0037] Wherein, the multi-dimensional deformation feature set is used to comprehensively describe a set of quantization parameters with different feature dimensions of the deformation characteristics of the support and protection structure, which is used to describe the deformation state of the overall and local support and protection structure from multiple angles.

[0038] Wherein, the overall deformation characteristics represent the attribute features reflecting the overall spatial form change trend of the large-area foundation pit support and protection structure from a macroscopic scale in the whole construction period, which is used to identify the overall stability and deformation trend of the whole support system, such as the horizontal displacement or vertical settlement mode of the whole top of the foundation pit along the peripheral range.

[0039] The local deformation characteristic represents a specific deformation behavior characteristic of a certain local area or component of the large-area foundation pit support and protection structure in the whole construction period, which is used to identify local stress abnormalities, local damage risks and the like, such as a sharp change in internal force of a certain steel support at a specific construction stage or a sudden increase in displacement of a local area of a wall.

[0040] The stage-by-stage evaluation represents a stage-by-stage and quantitative determination and analysis of the deformation state change of the large-area foundation pit support and protection structure in different construction stages, which is used to timely grasp the deformation control level of the support system in each construction stage, such as an independent evaluation of the stability of the foundation pit after the completion of the support to determine whether the next excavation stage can be entered.

[0041] The abnormal trend early warning represents an early discovery of potential abnormal development signs of the large-area foundation pit support and protection structure in the deformation behavior, which is used to provide preventive control measures for the construction management personnel, such as timely sending an alarm information and taking reinforcement measures when a local displacement acceleration growth trend is detected.

[0042] 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, which is used to ensure the structural safety and smooth progress of the construction plan in the construction process, such as adjusting the support installation sequence or densifying the monitoring point layout according to the local settlement evaluation result.

[0043] Exemplarily, first, around the time range of the whole construction period, 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 be subsequently identified regularly. Further, based on the important feature information extracted, each type of measurement curve in different construction stages in the dynamic measurement curve set is further subjected to pattern recognition processing, that is, by inducing the common characteristics of each measurement curve in terms of deformation trend and change amplitude, the overall deformation characteristic and the local deformation characteristic 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 period, so as to provide basic data basis and analysis reference for subsequent stage-by-stage evaluation, abnormal trend early warning and construction control decision.

[0044] The automatic measurement method of the deformation of the large-area foundation pit support and protection structure comprises the following steps.

[0045] In one exemplary embodiment, the initial measurement data set is processed by a time sequence division method 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 for different construction stages, including steps S201 to S203.

[0046] In step S201, the initial measurement data set is processed by node identification according to the construction condition change nodes reflected by each construction condition change time recorded in the construction log to obtain a node index set corresponding to the excavation completion node, support removal node and support replacement completion node of the large-area foundation pit support and protection structure.

[0047] The construction log represents a detailed construction activity schedule and operation content file recorded by the construction unit during the construction process of the large-area foundation pit, for example, a document recording the specific time and operation description when the excavation depth of the foundation pit reaches a certain design elevation.

[0048] The node index set represents a set of index data for identifying each construction condition change node, for example, setting index numbers on the initial measurement data set for the time points of excavation completion, support removal and support replacement completion.

[0049] The excavation completion node represents the construction condition change node when the excavation task of a certain stage in the construction process is completed, which is used to define the end point of an important construction interval, for example, the time point when the bottom of the foundation pit is excavated to the design elevation and is ready for support installation.

[0050] The support removal node represents a construction condition change node when the original support component is removed during the construction process, and is used to mark the time point of the local stress state change of the support structure, such as the time point of removing the previous temporary support before further excavation or support replacement.

[0051] The support replacement completion node represents a construction condition change node after the new support component is installed and fixed during the construction process, and is used to mark the time point of the support structure forming a complete stress system again, such as the time point of completing the reinforcement by the new support component after the original support fails.

[0052] For example, first, according to the construction activity progress information recorded in the construction log, the construction condition change time points directly related to the state change of the large-area foundation pit support structure are extracted; the construction log usually records events such as excavation completion, support installation or removal, support replacement completion in chronological order, so these 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, the construction condition change nodes are mapped to the measurement data in the initial measurement data set for node identification processing of the measurement data in the initial measurement data set; in 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 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.

[0053] In step S202, the initial measurement data set is divided into stages at the continuous construction condition change nodes according to the node index set, and a stage monitoring data set is obtained, which is bounded by adjacent construction condition change nodes, and includes monitoring data of each construction stage.

[0054] The stage monitoring data set represents a monitoring data sub-set corresponding to the continuous monitoring data collected in each construction stage, which is formed after the initial measurement data set is divided into stages according to the node index set, and is used to describe the deformation evolution process of the support structure in each construction stage, 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.

[0055] Exemplarily, the initial measurement data set is divided into stage intervals based on the node index set, i.e., each measurement data in the initial measurement data set is divided into several continuous but non-overlapping data subsets along the time axis with the adjacent two construction condition change nodes in the node index set as the boundary, and the stage monitoring data set is obtained by combining each data subset, so that the initial measurement data set containing each measurement data is converted into the stage monitoring data set containing the monitoring data of each construction stage. Correspondingly, the detection data can represent the data obtained by classifying the initial measurement data into the specified construction stage in the stage monitoring data set; wherein each data subset corresponds to a construction stage and contains the displacement change, internal force change and environmental change related monitoring data collected by all monitoring points in the construction stage. Moreover, in 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 node 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.

[0056] 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 and protection structure in different construction stages.

[0057] Exemplarily, the monitoring data of each construction stage in the stage monitoring data set is subjected to curve fitting processing, i.e., 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 used to model the curve shape, thereby generating a measurement curve that is continuous, smooth and can accurately reflect the actual change trend. Moreover, the corresponding construction stage and monitoring index should be labeled for the measurement curve fitted in different construction stages, so that the specific construction stage and physical meaning can be traced back based on the curve information in subsequent analysis. Based on this, the discrete monitoring data in the stage monitoring data set can be converted into a change trajectory with continuous time sequence characteristics, thereby forming a dynamic measurement curve set of the deformation evolution process of the large-area foundation pit support and protection structure covering each construction stage.

[0058] In this embodiment, firstly, the node identification processing of the construction condition change node is performed on the initial measurement data set, so that the key time nodes in the construction process can be determined to obtain the node index set, and the time sequence accuracy of subsequent data division is ensured; secondly, the stage interval division processing is performed on the initial measurement data set according to the node index set, so that the stage monitoring data set corresponding to the construction stage change can be formed, and the independence and continuity of the divided data are ensured; thirdly, the curve fitting processing is performed on the stage monitoring data set, so that the 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, and based on this, the accurate division and dynamic continuous modeling of the measurement data of the support and protection structure in the whole construction cycle are realized.

[0059] 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, and the stage monitoring data set bounded by adjacent construction condition change nodes is obtained, including steps S301 to S303.

[0060] 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, and the ordered time sequence set corresponding to the continuous construction condition change nodes is obtained, and the ordered time sequence set includes the node index identification corresponding to the continuous time section.

[0061] 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 time sequence arrangement of the construction condition change node.

[0062] Among them, the ordered time sequence set represents the node set arranged in time sequence after the time stamp field of the construction condition change node is sequentially arranged, which is used to define the time section of each continuous construction stage, such as the time sequence composed of the sequentially arranged excavation completion node, support removal node and support replacement completion node.

[0063] Among them, the time section represents the continuous time range determined by the adjacent two construction condition change nodes on the time axis in the ordered time sequence set, such as the time range between the excavation completion node and the adjacent support removal node.

[0064] Among them, the node index identification represents the independent index number allocated to each construction condition change node, which is used to quickly identify and locate the corresponding construction condition change node in the data processing process.

[0065] Exemplarily, firstly, in the node index set, each node index records a construction condition change node and a corresponding time stamp field, therefore, according to the time stamp field of the construction condition change node, all construction condition change nodes can be arranged in chronological order, thereby generating a continuous and ordered construction condition change node time sequence set, wherein each pair of adjacent construction condition change nodes defines a start and end time period of a construction stage; the chronological order not only reflects the natural evolution relationship of the construction process, but also provides a clear time limit basis for subsequent monitoring data division according to the construction logic. Furthermore, in the arrangement process, the node index should be ensured to be non-crossing and non-missing 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 in time section units, and each time section corresponds to two adjacent construction condition change nodes, which are marked by node index identification, so that the extraction of measurement data of each construction stage can be directly located and grouped according to the time section, and the basic contact between construction stage division and time data mapping is established.

[0066] In step S302, according to the time label of each measurement data in the initial measurement data set, each measurement data in the initial measurement data set is matched with the node index identification of the corresponding time section in the ordered time sequence set, to obtain each stage monitoring data sub-set divided according to the continuous time section.

[0067] Wherein, the time label of the measurement data represents the collection time information attached to each measurement data record.

[0068] Wherein, the stage monitoring data sub-set represents the data sub-set formed by merging the measurement data belonging to the same construction stage 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 next step of excavation completion belong to a certain stage monitoring data sub-set.

[0069] Exemplarily, each measurement data in the initial measurement data set carries a corresponding time label, by reading the time label, it can be compared one by one with each time segment in the ordered time sequence set, that is, in the comparison process, it should be judged according to the time size relationship which continuous time segment each measurement data belongs to corresponds to the construction stage. Based on this, a large number of measurement data originally spanning the entire construction period can be accurately divided into each continuous construction stage according to the time division standard of construction progress, so as to form a phased monitoring data sub-set organized according to time segments; Each phased monitoring data sub-set only contains data records collected in the corresponding construction stage, and different phased monitoring data sub-sets do not overlap, maintaining the independence and continuity of time.

[0070] Step S303, according to the hierarchical index mapping structure established based on the key-value pair of node index identifier and measurement data identifier, composite index processing is performed on each phased monitoring data sub-set to obtain a phased monitoring data set bounded by adjacent construction condition change nodes.

[0071] Among them, the measurement data identifier represents the identifier information for uniquely identifying each measurement data in the initial measurement data set.

[0072] 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 phased monitoring data sub-sets and realize efficient access according to construction phase and measurement data dual dimensions, for example, the upper layer is classified by node index identifier, and the lower layer points to specific data by measurement data identifier.

[0073] Exemplarily, on the basis of obtaining each phased monitoring data sub-set divided by time segments, it is necessary to further establish an effective index management mechanism to support subsequent rapid retrieval and analysis of each stage data. Specifically, by combining the node index identifier and the measurement data identifier, a hierarchical index mapping structure is constructed, which 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. Thus, each phased monitoring data sub-set is organized into a composite index system with unified retrieval logic. Finally, through the above composite index processing of each phased monitoring data sub-set, a phased monitoring data set is obtained, which is bounded by adjacent construction condition change nodes, complete in content and efficient in retrieval.

[0074] In this embodiment, first, the node index set is sequentially arranged to obtain an ordered time sequence set, so as to determine the time sequence relationship between the construction condition change nodes and ensure the continuity and time sequence logic of the stage division; second, the time tags of each measurement data in the initial measurement data set are matched with the ordered time sequence set to obtain a stage monitoring data subset divided according to continuous time sections, so as to realize accurate attribution of the measurement data and ensure complete data and clear boundaries in each construction stage; third, a hierarchical index mapping structure is established according to the combination of the node index and the measurement data identifier, so as to orderly manage the stage monitoring data subset to obtain the stage monitoring data set, form a clear and systematic construction stage measurement data organization method, and improve the efficiency of subsequent data retrieval and calling.

[0075] In one exemplary embodiment, 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 steps S401 to S403.

[0076] Step S401, 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 with uniform time distribution in each construction stage, and the stage resampling data set includes resampling data of each physical quantity in different construction stages.

[0077] Among them, the stage resampling data set represents a data set obtained by resampling the original stage 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.

[0078] 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.

[0079] Step S402, in the stage resampling data set, the resampling data of each physical quantity in different construction stages is respectively subjected to curve fitting processing, 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, and the stage fitting curve set includes stage fitting curves of each physical quantity in different construction stages.

[0080] 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.

[0081] Among them, the displacement type monitoring quantity represents a physical quantity used to measure the spatial displacement change of the supporting and supporting structure in 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.

[0082] 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 in 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.

[0083] 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 in 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.

[0084] 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 type 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 respectively 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.

[0085] 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.

[0086] 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 type 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.

[0087] 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.

[0088] In an 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.

[0089] 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.

[0090] 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.

[0091] The deformation extreme value feature point represents a 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.

[0092] The deformation rate mutation feature point represents a position point where the deformation rate, i.e. the curve slope, in the dynamic measurement curve set suddenly changes, 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.

[0093] The deformation trend inflection point represents a 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Based on this, the set of deformation mode parameters mapped to the corresponding physical space position can be divided into data contained in several spatial regions with similar deformation behavior characteristics, and the data aggregated in each spatial region exhibits consistent or highly correlated deformation characteristics. Finally, based on these spatial 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.

[0104] In this embodiment, first, full-cycle feature point extraction processing is performed on the set of dynamic measurement curves, so that a set of key feature points 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 set of dynamic measurement curves, so that the overall deformation trend mode and the local deformation behavior mode can be summarized, and a set of deformation mode parameters 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 set of deformation mode parameters, 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.

[0105] 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 set of dynamic measurement curves, and a set of deformation mode parameters representing the overall deformation trend mode and the local deformation behavior mode is obtained, including steps S601 to S602.

[0106] Step S601, according to the deformation main direction, main deformation rate and overall deformation amplitude reflected in the key feature point set, global feature extraction processing is performed on each measurement curve corresponding to different construction stages, and a set of deformation mode parameters representing the overall deformation trend mode is obtained.

[0107] 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.

[0108] The main deformation rate represents the main deformation development speed level exhibited by the support and protection structure in the construction stage during 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.

[0109] The overall deformation range represents a variation 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 a deformation amount of the support and protection structure under a stress response, for example, a variation range of a horizontal displacement of a top of a foundation pit slope in a stage.

[0110] 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 a deformation process of the support and protection structure in a specific construction stage, and therefore, in the global feature extraction, systematic analysis is performed around the feature indexes related to global analysis such as the main deformation direction, the main deformation rate and the overall deformation range represented by the key feature points. Specifically, the main deformation direction is used to quantify a main deformation development 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 a 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 a maximum deformation degree of the support and protection structure in the entire construction stage.

[0111] Based on the feature indexes reflected by the key feature points, a full-range analysis is performed on each measurement curve, and a numerical feature corresponding to the feature indexes is extracted, forming a deformation mode parameter set representing an overall deformation trend mode.

[0112] 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, and a deformation mode parameter set representing a local deformation behavior mode is obtained.

[0113] The change rate abnormality degree represents a degree of abnormal fluctuation of the deformation rate of the support and protection structure relative to the average rate in the construction stage, and is used to identify a violent deformation behavior of the support and protection structure caused by a stress or working condition change in a short time, for example, a case where a horizontal displacement rate of a local area of a support wall significantly increases due to excavation work.

[0114] The trend turning frequency represents a number of times of change of the deformation trend direction of the support and protection structure in the construction stage, and is used to reflect a complexity of deformation trend change of the support and protection structure under the action of multiple disturbance factors in the construction process, for example, a number of times of rising and falling turning of a deformation curve of the support and protection structure under the alternating action of support construction and dewatering work.

[0115] Exemplarily, after the global feature extraction is completed, further processing of local feature extraction is performed on each measurement curve in the dynamic measurement curve set 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.

[0116] Based on the feature indexes reflected by the key feature points, the whole analysis of each measurement curve is performed, the numerical features corresponding to the feature indexes are extracted, and the deformation mode parameter set representing the local deformation behavior mode is formed.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The spatial distance matrix represents a matrix indexed by pairs of monitoring points and elements of spatial distance, 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.

[0121] Exemplarily, first, according to the spatial coordinate data of each monitoring point in the multi-class monitoring point group, the spatial relationship between each monitoring point is analyzed, that is, based on the spatial geometric principle, the actual spatial distance value between two monitoring points is obtained by extracting the coordinate component difference of any two monitoring points and using the Euclidean distance formula or other standard distance calculation method. Further, by calculating the distance between all pairs of monitoring points in the multi-class monitoring point group, a complete spatial distance matrix can be formed, each element in the spatial distance matrix corresponding 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 the monitoring point dense area, sparse area and local abnormal distribution condition, etc.

[0122] In step S702, the spatial distance matrix and the deformation mode parameter set are processed for similarity matching according to a weighted fusion mode combining spatial distance and mode feature distance, to obtain a similarity score matrix.

[0123] The weighted fusion mode combining spatial distance and mode feature distance means that after the spatial distance and the deformation mode feature distance between the monitoring points are normalized, the weighted sum is calculated according to the set weight coefficient, so as to comprehensively reflect the comprehensive similarity degree of the monitoring point pair in terms of spatial position proximity and deformation feature similarity, which is used to construct a unified dimension similarity evaluation basis.

[0124] The similarity score matrix represents a matrix formed after calculation based on the weighted fusion mode combining spatial distance and mode feature distance. Each element in the matrix represents the comprehensive similarity score of a pair of monitoring points in terms of spatial distribution and deformation feature, for example, the higher the score, the more similar the two monitoring points are in terms of spatial distribution and deformation characteristics.

[0125] The mode feature distance is represented based on the numerical difference between the feature parameters extracted from the deformation mode parameter set of different monitoring points, which is used to quantify the feature similarity degree of different monitoring points in terms of overall deformation trend mode and local deformation behavior mode. For example, the smaller the numerical difference between two monitoring points in terms of deformation main direction, main deformation rate and change rate anomaly degree, the smaller the mode feature distance, and the greater the feature similarity degree in terms of overall deformation trend mode and local deformation behavior mode.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Exemplarily, firstly, the specific positions of the monitoring points in the physical space are determined based on the spatial coordinate data of the monitoring points, and are taken as the spatial reference basis for subsequent clustering processing; then, the feature parameter data corresponding to each monitoring point in the deformation mode parameter set is accurately mapped to the position of the monitoring point in the physical space, establishing a one-to-one correspondence between the deformation features and the spatial positions. On this basis, instead of directly clustering the monitoring points themselves, the feature parameter data mapped to the spatial positions is subjected to in-depth analysis and classification; specifically, the similarity of the feature parameter data between adjacent monitoring points needs to be analyzed, and the similarity analysis content can include: the consistency of the deformation trend, i.e. the consistency degree of the main direction and the main deformation rate of each monitoring point; the proximity of the extreme position, i.e. the consistency of the period when the maximum or minimum deformation occurs; the synchronization of the deformation rate change, i.e. the coordination degree of the rate change in the deformation development process.

[0131] By comprehensively judging the similarity of the above-mentioned multiple feature dimensions and combining the straight line distance relationship between the monitoring points in the physical space, grouping and classifying processing is performed according to the preset similarity standard; in the specific clustering process, the preset density peak clustering algorithm is adopted to determine the monitoring point group with high similarity and high density under the dual constraints of spatial distance and mode feature distance, forming a clustering cluster that is continuous in space and consistent in deformation features; the feature parameter data within each clustering cluster exhibits highly consistent overall and local deformation characteristics, while the feature parameter data between different clustering clusters has obvious differences in deformation characteristics or spatial positions. Finally, based on the deformation mode features represented by each clustering cluster, a multi-dimensional deformation feature set is systematically induced and formed for comprehensively describing the overall and local differential deformation characteristics of the large-area foundation pit support and protection structure in the whole construction cycle.

[0132] In this embodiment, firstly, the spatial distance matrix calculation processing is performed on the spatial coordinate data of the multiple types of monitoring point groups, so as to accurately reflect the physical space distribution relationship between the monitoring points; secondly, the similarity matching processing of the spatial distance matrix and the deformation mode parameter set is performed according to the weighted fusion mode combining the spatial distance and the mode feature distance to obtain a similarity score matrix, so as to comprehensively depict the correlation degree of the monitoring points in the spatial positions and the deformation characteristics; thirdly, the spatial clustering processing of the similarity score matrix is performed according to the density peak clustering algorithm, so as to realize the ordered grouping and classification of the feature parameter data in the deformation mode parameter set, extract the overall and local deformation characteristics to obtain a multi-dimensional deformation feature set, and realize the spatial correlation recognition and systematic modeling of the overall and local deformation characteristics of the large-area foundation pit support and protection structure.

[0133] 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 explicitly stated 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 part of other steps or steps or stages in other steps.

[0134] 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.

[0135] 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:

[0136] The acquisition module 201 is configured to perform synchronous data acquisition and processing on a plurality of types 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 types of monitoring point groups include displacement type monitoring points, internal force type monitoring points, and environmental type monitoring points.

[0137] 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 large-area foundation pit support structure deformation evolution process for different construction stages.

[0138] The mode recognition module 203 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, 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.

[0139] In an exemplary embodiment, the curve fitting module 202 is further configured to: perform node identification processing on the initial measurement data set according to the construction condition change nodes reflected by the construction condition change time points recorded in the construction log, to obtain a node index set corresponding to the excavation completion node, the support removal node and the support replacement completion node of the large-area foundation pit support and protection structure; 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 deformation evolution process of the large-area foundation pit support and protection structure in different construction stages.

[0140] In an exemplary embodiment, the curve fitting module 202 is further configured to: perform sequential arrangement processing on the node index set according to the time stamp field corresponding to each construction condition change node, 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 each measurement data in the initial measurement data set 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, to obtain each stage monitoring data sub-set divided according to the continuous time sections; and perform composite index processing on each stage monitoring data sub-set according to the hierarchical index mapping structure established based on the key-value pairs of the node index identifier and the measurement data identifier, to obtain the stage monitoring data set bounded by adjacent construction condition change nodes.

[0141] In an exemplary embodiment, the curve fitting module 202 is further configured to: perform interval resampling processing on the monitoring data corresponding to each physical quantity in the phased monitoring data set according to a preset time interval, to obtain a phased resampling data set in which the time distribution of each physical quantity is uniform in each construction stage, the phased resampling data set including the resampling data of each physical quantity in different construction stages; perform curve fitting processing on the resampling data of each physical quantity in different construction stages in the phased resampling data set, to obtain a set of phased fitting curves for displacement monitoring quantities, internal force monitoring quantities and environmental monitoring quantities in different construction stages, the set of phased fitting curves including the phased fitting curves of each physical quantity in different construction stages; and integrate and organize the phased fitting curves corresponding to different physical quantities in the same construction stage to obtain a set of dynamic measurement curves for the deformation evolution process of large-area foundation pit support and retaining structure in different construction stages.

[0142] In an exemplary embodiment, the pattern recognition module 203 is further configured to: perform full-cycle feature point extraction processing on the dynamic measurement curve set to obtain a key feature point set covering the deformation process throughout the entire construction cycle, wherein the key feature points in the key feature point set include deformation extreme value feature points, deformation rate abrupt change feature points, and deformation trend inflection points; based on the key feature point set, perform deformation pattern recognition processing on each measurement curve corresponding to different construction stages in the dynamic measurement curve set to obtain a deformation pattern parameter set characterizing the overall deformation trend pattern and local deformation behavior pattern; and based on the similarity between the spatial coordinate data of multiple monitoring point groups and the deformation pattern parameter set, perform spatial feature aggregation processing on the deformation pattern parameter set to obtain a multi-dimensional deformation feature set used to comprehensively describe the overall deformation characteristics and local differential deformation characteristics of the large-area foundation pit support structure.

[0143] In an exemplary embodiment, the pattern recognition module 203 is further configured to: perform global feature extraction processing on each measurement curve corresponding to different construction stages based on the main deformation direction, main deformation rate, and overall deformation amplitude reflected by the key feature points in the key feature point set, to obtain a set of deformation mode parameters characterizing the overall deformation trend pattern; and perform local feature extraction processing on each measurement curve corresponding to different construction stages based on the anomaly degree of change rate and the frequency of trend reversal reflected by the key feature points in the key feature point set, to obtain a set of deformation mode parameters characterizing the local deformation behavior pattern.

[0144] In an example embodiment, the mode recognition module 203 is further configured to: calculate spatial straight-line distances between pairs of monitoring points of 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 spatial distribution relationships of the monitoring points; perform similarity matching processing on the spatial distance matrix and the deformation mode parameter set according to a weighted fusion mode combining the spatial distance and the mode 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 overall deformation characteristics and local differential deformation characteristics of the large-area foundation pit support and protection structure.

[0145] The modules in the automatic measurement system for deformation of the large-area foundation pit support and protection structure can be implemented wholly or partially by software, hardware, or a combination thereof. The modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in the computer device in software form, so as to be called and executed by the processor to perform operations corresponding to the modules.

[0146] In an example embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in any of the above embodiments when executing the computer program.

[0147] In an example embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in any of the above embodiments when executing the computer program.

[0148] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in 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 block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose 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., without being limited thereto.

[0149] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0150] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An automatic measurement method for the deformation of a large-area foundation pit support structure, characterized in that, The method includes: Synchronous data acquisition and processing are performed on multiple monitoring point groups set up 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. The multiple monitoring point groups include displacement monitoring points, internal force monitoring points and environmental monitoring points. Based on the time series division method indexed by the construction condition change node, the initial measurement data set is processed by stage grouping to obtain a set of dynamic measurement curves for the deformation evolution process of large-area foundation pit support structure at different construction stages. Based on the dynamic measurement curve set, pattern recognition processing is performed on the deformation behavior of the large-area foundation pit support structure throughout the construction cycle to obtain a multi-dimensional deformation feature set that describes the overall and local deformation characteristics of the large-area foundation pit support structure. The multi-dimensional deformation feature set is used to realize the phased assessment of the deformation state of the large-area foundation pit support structure, early warning of abnormal trends, and construction control decisions.

2. The method according to claim 1, characterized in that, The initial measurement data set is divided into stages based on a time-series partitioning method indexed by construction condition change nodes. This results in a set of dynamic measurement curves representing the deformation evolution of large-area foundation pit support structures at different construction stages, including: Based on the construction condition change nodes reflected by the various construction condition change times recorded in the construction log, the initial measurement data set is processed by node identification to obtain a set of node indexes corresponding to the excavation completion node, support removal node, and support replacement completion node of the large-area foundation pit support and support structure. The initial measurement data set is divided into stages based on the node index set at continuous construction condition change nodes to obtain a staged monitoring data set with adjacent construction condition change nodes as boundaries. The staged monitoring data set includes monitoring data for each construction stage. Curve fitting is performed on the monitoring data of each construction stage in the aforementioned phased monitoring data set to obtain a set of dynamic measurement curves for the deformation evolution process of large-area foundation pit support and retaining structures at different construction stages.

3. The method according to claim 2, characterized in that, The step of dividing the initial measurement data set into stage intervals based on the node index set at consecutive construction condition change nodes to obtain a staged monitoring data set bounded by adjacent construction condition change nodes includes: According to the timestamp field corresponding to each construction condition change node, the node index set is sequentially arranged to obtain an ordered time series set corresponding to consecutive construction condition change nodes. The ordered time series set includes node index identifiers corresponding to consecutive time segments. Based on the time label of each measurement data in the initial measurement data set, each measurement data in the initial measurement data set is matched with the node index identifier of the corresponding time segment in the ordered time series set to obtain each stage monitoring data subset divided according to continuous time segments; Based on the hierarchical index mapping structure established by combining key-value pairs of node index identifiers and measurement data identifiers, the various phase monitoring data subsets are subjected to composite indexing to obtain a phase monitoring data set with adjacent construction condition change nodes as boundaries.

4. The method according to claim 2, characterized in that, The monitoring data for each construction stage in the phased monitoring data set are 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 for different construction stages, including: In the phased monitoring data set, the monitoring data corresponding to each physical quantity are resampled at fixed intervals according to a preset time interval to obtain a phased resampled data set in which each physical quantity is evenly distributed in time during each construction stage. The phased resampled data set includes resampled data of each physical quantity in different construction stages. In the set of phased resampling data, curve fitting processing is performed on the resampling data of each physical quantity at different construction stages to obtain a set of phased fitting curves for displacement monitoring quantities, internal force monitoring quantities and environmental monitoring quantities at different construction stages. The set of phased fitting curves includes the phased fitting curves of each physical quantity at different construction stages. By integrating and organizing the stage-specific fitting curves corresponding to different physical quantities within the same construction stage, a set of dynamic measurement curves for the deformation evolution process of large-area foundation pit support structures at different construction stages is obtained.

5. The method according to claim 1, characterized in that, The deformation behavior of the large-area foundation pit support structure during the entire construction cycle is processed by pattern recognition based on the dynamic measurement curve set to obtain a multi-dimensional deformation feature set describing the overall and local deformation characteristics of the large-area foundation pit support structure, including: The dynamic measurement curve set is processed by full-cycle feature point extraction to obtain a set of key feature points covering the deformation process throughout the entire construction cycle. The key feature points in the set of key feature points include deformation extreme value feature points, deformation rate abrupt change feature points, and deformation trend inflection points. Based on the set of key feature points, deformation pattern 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 pattern parameters that characterize the overall deformation trend pattern and the local deformation behavior pattern. Based on the similarity between the spatial coordinate data of multiple monitoring point groups 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 large-area foundation pit support structure.

6. The method according to claim 5, characterized in that, The process involves performing deformation pattern recognition processing on each measurement curve corresponding to different construction stages in the dynamic measurement curve set based on the set of key feature points, to obtain a set of deformation pattern parameters characterizing the overall deformation trend pattern and local deformation behavior pattern, including: Based on the main deformation direction, main deformation rate and overall deformation amplitude reflected by the key feature points in the set of key feature points, global feature extraction processing is performed on each measurement curve corresponding to different construction stages to obtain a set of deformation mode parameters that characterize the overall deformation trend. Based on the rate of change anomaly and trend reversal frequency reflected by the key feature points in the set of key feature points, local feature extraction processing is performed on each measurement curve corresponding to different construction stages to obtain a set of deformation mode parameters characterizing the local deformation behavior pattern.

7. The method according to claim 5, characterized in that, Based on the similarity between the spatial coordinate data of multiple monitoring point groups 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 large-area foundation pit support structures, including: Based on the spatial coordinate data of the multi-type monitoring point groups, the spatial straight-line distance between the monitoring point pairs of the multi-type monitoring point groups is calculated to obtain a spatial distance matrix reflecting the spatial distribution relationship of each monitoring point. Based on a weighted fusion method combining spatial distance and pattern feature distance, a similarity matching process is performed on the spatial distance matrix and the deformable pattern parameter set to obtain a similarity score matrix; Based on the preset density peak clustering algorithm, the similarity score matrix is ​​spatially clustered to obtain a multidimensional deformation feature set for comprehensively describing the overall deformation characteristics and local differential deformation characteristics of the large-area foundation pit support structure.

8. An automatic measurement system for the deformation of a large-area foundation pit support structure, characterized in that, The system includes: The acquisition module is used to synchronously acquire and process data from multiple monitoring points set up according to the large-area foundation pit support structure, and obtain an initial set of measurement data to characterize the deformation state of the large-area foundation pit support structure. The multiple monitoring points include displacement monitoring points, internal force monitoring points and environmental monitoring points. The curve fitting module is used to perform phased grouping processing on the initial measurement data set based on the time series division method indexed by the construction condition change nodes, so as to obtain a set of dynamic measurement curves for the deformation evolution process of large-area foundation pit support structure at different construction stages. The pattern recognition module is used to perform pattern recognition processing on the deformation behavior of the large-area foundation pit support structure during the entire construction cycle based on the dynamic measurement curve set, and obtain a multi-dimensional deformation feature set to describe the overall deformation characteristics and local deformation characteristics of the large-area foundation pit support structure. The multi-dimensional deformation feature set is used to realize the phased assessment of the deformation state of the large-area foundation pit support structure, early warning of abnormal trends, and construction control decisions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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

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