Monitoring, prevention and control method and system for road construction process

Abstract

The invention relates to the technical field of construction monitoring, in particular to a monitoring, prevention and control method and system for a road construction process, and the method comprises the steps: carrying out laser ranging and vibration signal analysis, extracting phase disturbance and tail section response features, recognizing a key monitoring section, carrying out time mutual dependence analysis, calibrating a risk section, and carrying out prevention and control alarm. According to the invention, through the spatial distribution fitting constructed by laser ranging and the calculation of the phase disturbance density index, the spatial resolution capability of the disturbance anomaly of the underground structure is improved, the spatial turn-back rate is analyzed in combination with the change of the included angle of the coordinate vector, the abnormal disturbance area is clustered and aggregated through the density peak value, and the accurate screening of the observation section is realized. According to the method, a tail section vibration signal collected by an underground wall acceleration sensor is introduced, a feature vector sequence is constructed according to the tail section duration and the energy residual proportion, and the vibration difference between sections is described through the Mahalanobis distance, so that the sensitive recognition capability of construction disturbance is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of construction monitoring, and in particular to a method and system for monitoring and preventing road construction processes. Background Art

[0002] The technical field of construction monitoring includes related technical means for obtaining, recording, and evaluating various environmental, structural, or equipment state parameters during the construction process in real time. The core content of this technical field is to continuously observe and collect data on multi-source physical information such as vibration, displacement, tilt, stress, noise, etc. at the construction site, so as to continuously master the structural safety, environmental impact, and operation stability during the construction process.

[0003] Among them, the method for monitoring and preventing road construction processes refers to aiming at risk sources such as ground disturbances, structural deformations, and abnormal vehicle operations during road construction processes. By deploying various sensing devices such as ground settlement meters, laser rangefinders, and inertial navigation devices, data is collected on the road surface, underground structures, and the state of construction machinery, and relying on modeling and comparison methods that integrate measurement parameters such as displacement information, vibration data, and construction time series, key nodes and potential abnormal factors during the construction process are identified.

[0004] Existing methods based on single-point monitoring or simple displacement comparison have limitations in spatial modeling capabilities, lack comprehensive sensing means for multi-dimensional parameters between cross-sectional structures, resulting in one-sided abnormal identification and difficulty in discovering potential coupled disturbance characteristics. After collecting data using various types of sensing devices, a collaborative fusion mechanism has not been formed, making the structural state information present a discrete distribution and unable to effectively reflect the propagation trajectory of the disturbance source in the spatial and temporal dimensions. Some existing methods are based on static data comparison, ignoring the dynamic evolution characteristics of vibration signals and the tail section response law, resulting in lagging feature recognition and difficulty in capturing the deep abnormal paths caused by construction. During the analysis of construction time series, the time interdependence relationship between disturbances and responses is not considered, and the risk paths cannot be classified, restricting the judgment of the risk evolution trend. For example, during the construction of diaphragm walls, the potential risk areas cannot be judged based on the changes in vibration characteristics of adjacent cross-sections, resulting in delayed warnings and high safety hazards. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose a method and system for monitoring and preventing road construction processes.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A method for monitoring and preventing road construction processes includes the following steps: S1: Monitor the distance phase change between monitoring points around the underground structure and the position of the observation section during the construction of the road foundation pit through laser ranging, perform spatial distribution fitting processing, and calculate the phase perturbation density index of each observation section; S2: Call the coordinate data of the monitoring points, construct the center line vector of the continuous observation section position, calculate the angle change between the vectors as the spatial turning rate, perform density peak clustering on the phase perturbation density index of each observation section and the spatial turning rate of the corresponding observation section, and obtain the abnormal perturbation section set; S3: Obtain the vibration signal recorded by the underground wall acceleration sensor, extract the duration of the signal tail part and the proportion of energy residue, judge the characteristic difference degree between adjacent sections, and obtain the tail section abnormal response section set; S4: Perform cross-classification identification on the abnormal perturbation section set and the tail section abnormal response section set, and screen the key monitoring section set; S5: Obtain the disturbance start time and the starting time of the vibration tail section of the monitoring points in the key monitoring section set, perform information interdependence analysis on the time synchronization relationship, calibrate the risk sections of the construction disturbance monitoring, and issue response prevention and control warnings.

[0007] As a further solution of the present invention, the phase perturbation density index includes the disturbance gradient distribution value, the disturbance intensity of a specified length, and the observation section coordinate sequence. The abnormal perturbation section set is specifically the abnormal section number, the section spatial offset direction, and the section aggregation density coefficient. The tail section abnormal response section set includes the energy residue index, the wave tail duration, and the vibration difference degree between sections. The key monitoring section set specifically refers to the section risk ranking label, the four-dimensional feature combination value, and the section identification stability level. The risk sections of the construction disturbance monitoring include the first-level risk section, the second-level risk section, and the early warning attention section.

[0008] As a further solution of the present invention, the specific steps for obtaining the phase perturbation density index are as follows: S111: Obtain the laser ranging phase difference between monitoring points around the underground structure during the construction of the road foundation pit and the position of the observation section corresponding to the monitoring points, construct a two-dimensional coordinate distribution area based on the coordinate relationship of the monitoring points, and generate a monitoring point position mapping diagram; S112: Based on the monitoring point position mapping diagram and the corresponding phase difference data, perform spatial interpolation processing in the continuous area, establish the phase change value distribution corresponding to each interpolation point, and obtain a two-dimensional phase change field; S113: According to the phase change value along the direction of the underground continuous wall in the two-dimensional phase change field and the physical length between adjacent monitoring points in each observation section, calculate the ratio of the phase change value to the physical length to obtain the phase perturbation density index.

[0009] As a further solution of the present invention, the steps for obtaining the abnormal disturbance section set are specifically as follows: S211: Call the coordinate data of the monitoring points, extract the geometric centers of the middle monitoring points of every three consecutive sections in the order of the observation section numbers, construct two direction vectors based on the coordinates of adjacent geometric centers, and calculate the angle change value of the two direction vectors to obtain a spatial turning rate sequence; S212: Call the spatial turning rate sequence and the phase disturbance density index corresponding to each observation section, screen the observation section numbers that meet the spatial turning rate threshold and the disturbance density threshold, and obtain a double-threshold satisfaction section group; S213: According to the spatial distribution density of each section in the double-threshold satisfaction section group, perform a spatial distribution density peak clustering operation, classify and aggregate the section numbers, and obtain an abnormal disturbance section set.

[0010] As a further solution of the present invention, the steps for obtaining the abnormal response section set in the tail section are specifically as follows: S311: Obtain the vibration signals recorded by the underground wall acceleration sensors, identify the termination point of the main frequency energy attenuation, and extract the tail section vibration signals backward from the termination point. Respectively, count the tail section duration and the ratio of the tail section vibration energy to the total energy to obtain a vibration tail section feature vector sequence; S312: According to the vibration tail section feature vector sequence, arrange the vector sequence in the order of the section numbers along the underground continuous wall, and calculate the Mahalanobis distance based on the tail section feature vectors of each pair of adjacent sections to obtain a section-to-section vibration difference degree sequence; S313: Call the section-to-section vibration difference degree sequence, and combine the tail section energy residue ratio and the change trend of the tail section duration to screen the section numbers to obtain an abnormal response section set in the tail section.

[0011] As a further solution of the present invention, the steps for obtaining the key monitoring section set are specifically as follows: S411: Based on the section number data of the abnormal disturbance section set and the abnormal response section set in the tail section, extract the section number sequence that exists in both sets at the same time to generate an intersection section number set; S412: According to the intersection section number set, extract the phase disturbance density index, spatial turning rate, tail section duration, and energy residue ratio corresponding to each section to obtain a section joint feature vector sequence; S413: Perform unsupervised classification processing on the section joint feature vector sequence, and screen the section numbers with a feature distribution concentration higher than the concentration threshold to obtain a key monitoring section set.

[0012] As a further solution of the present invention, the steps for obtaining the risk section of the construction disturbance monitoring are specifically as follows: S511: Obtain the disturbance start time and the vibration tail section start time of each monitoring point in the key monitoring section set, perform time axis alignment processing on the two types of time series in sequence according to the section number order, calculate the difference between the disturbance time and the response time of each group of sections, and generate a section disturbance-response time difference sequence; S512: Call the section disturbance-response time difference sequence and the two types of original time series, calculate the maximum mutual information value, the average mutual information value and the maximum time difference for each continuous path segment, and obtain a set of path segment information interdependence parameters; S513: According to the mutual information value and the maximum time difference of each path segment in the set of path segment information interdependence parameters, perform a section grading judgment operation to obtain the risk sections of construction disturbance monitoring and perform response prevention and control warnings.

[0013] A monitoring and prevention and control system for road construction processes, the system comprising: The phase density calculation module monitors the distance phase change and the observation section position between the monitoring points around the underground structure during the road foundation pit construction process through laser ranging, performs spatial distribution fitting processing, and calculates the phase disturbance density index of each observation section; The turning-back rate clustering module calls the coordinate data of the monitoring points, constructs a central line vector of the continuous observation section positions, calculates the change in the angle between the vectors as the spatial turning-back rate, and performs density peak clustering on the phase disturbance density index of each observation section and the spatial turning-back rate of the corresponding observation section to obtain an abnormal disturbance section set; The vibration tail section extraction module obtains the vibration signal recorded by the underground wall acceleration sensor, extracts the duration and the energy residue ratio of the signal wave tail part, and judges the characteristic difference degree between adjacent sections to obtain a tail section abnormal response section set; The abnormal intersection recognition module performs cross-classification recognition on the abnormal disturbance section set and the tail section abnormal response section set to screen a key monitoring section set; The risk calibration warning module obtains the disturbance start time and the vibration tail section start time of the monitoring points in the key monitoring section set, performs information interdependence analysis on the time synchronization relationship, calibrates the risk sections of construction disturbance monitoring and performs response prevention and control warnings.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, through the spatial distribution fitting constructed by laser ranging and the calculation of the phase perturbation density index, the spatial resolution ability for underground structure perturbation anomalies is improved. Combining the spatial turning rate analyzed from the change in the included angle of coordinate vectors, the abnormal perturbation regions are aggregated through density peak clustering, realizing the precise screening of the observation section. The vibration signals of the tail section collected by the underground wall acceleration sensor are introduced. According to the duration of the tail section and the proportion of energy residue, a feature vector sequence is constructed, and the vibration difference between sections is characterized by the Mahalanobis distance, enhancing the sensitive recognition ability for construction perturbations. Based on the intersection of the abnormal perturbation section and the tail section abnormal response section, a joint feature vector of phase perturbation density, turning rate, and vibration tail section characteristics is introduced, and the precise calibration of the feature concentrated section is realized based on unsupervised clustering, improving the accuracy and stability of risk recognition. By extracting the time series data of key monitoring sections, calculating the time difference between perturbations and responses, and analyzing the information dependence degree of the path section in combination with the maximum mutual information and average mutual information parameters, the risk sections are effectively divided and the prevention and control warning of the response level is realized. The overall process forms multi-layer collaboration in dimensions such as information acquisition accuracy, feature mining depth, and time series dependence judgment, expanding the boundaries of traditional monitoring means in recognition accuracy and response efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic diagram of the main steps of the present invention; Figure 2 is a flowchart of step S1 of the present invention; Figure 3 is a flowchart of step S2 of the present invention; Figure 4 is a flowchart of step S3 of the present invention; Figure 5 is a flowchart of step S4 of the present invention; Figure 6 is a flowchart of step S5 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0018] Please refer to Figure 1 , the present invention provides a technical solution: a method for monitoring and preventing road construction processes, including the following steps: S1: Monitor the distance phase change between monitoring points around the underground structure and the position of the observation section during the construction of the road foundation pit by laser ranging, perform spatial distribution fitting processing, and calculate the phase perturbation density index of each observation section; S2: Call the coordinate data of the monitoring points, construct the center line vector of the continuous observation section position, calculate the angle change between the vectors as the spatial turning rate, perform density peak clustering on the phase perturbation density index of each observation section and the spatial turning rate of the corresponding observation section to obtain the abnormal perturbation section set; S3: Obtain the vibration signal recorded by the underground wall acceleration sensor, extract the duration and the proportion of energy residue at the tail part of the signal wave, judge the characteristic difference degree between adjacent sections to obtain the tail section abnormal response section set; S4: Perform cross-classification identification on the abnormal perturbation section set and the tail section abnormal response section set to screen the key monitoring section set; S5: Obtain the disturbance start time and the starting time of the vibration tail section of the monitoring points in the key monitoring section set, perform information interdependence analysis on the time synchronization relationship, calibrate the risk sections of the construction disturbance monitoring and perform response prevention and control warnings; The phase perturbation density index includes the disturbance gradient distribution value, the disturbance intensity of a specified length, and the observation section coordinate sequence. The abnormal perturbation section set specifically includes the abnormal section number, the section spatial offset direction, and the section aggregation density coefficient. The tail section abnormal response section set includes the energy residue index, the wave tail duration, and the vibration difference degree between sections. The key monitoring section set specifically refers to the section risk ranking label, the four-dimensional feature combination value, and the section identification stability level. The risk sections of the construction disturbance monitoring include the first-level risk section, the second-level risk section, and the early warning attention section.

[0019] Please refer to Figure 2 , the specific steps for obtaining the phase perturbation density index are as follows: S111: Obtain the laser ranging phase difference between monitoring points around the underground structure during the construction of the road foundation pit and the observation section position corresponding to the monitoring points. Based on the coordinate relationship of the monitoring points, construct a two-dimensional coordinate distribution area and generate a monitoring point position mapping diagram. First, synchronously collect the phase differences between preset monitoring points located around the diaphragm wall based on a laser rangefinder. A typical setting is to arrange 5 monitoring points P1 to P5 along the retaining structure at the deep foundation pit construction site. The ranging time interval is set to 30 minutes, and the data collection period is 7 days. During the ranging process, record the phase change generated by the optical path propagation between the monitoring points through laser interference technology. The first measured phase difference between P1 and P2 is 120°, corresponding to a laser wavelength of λ = 1550 nm. Then, the initial optical path difference is calculated as follows. Let the phase difference be , then , where : represents the optical path difference between two monitoring points (unit: meter), : represents the laser wavelength, and in this example, it is taken as , : represents the measured phase difference (unit: degree), which is 120° here. Substituting the values, we get: . Then, according to the coordinate data of each monitoring point measured in advance by a total station or an RTK high-precision positioning system, map it to the corresponding position in the two-dimensional plane area. For example, let the coordinates of P1 to P5 be (0, 0), (1.5, 0), (3, 0.5), (4.5, 1), (6, 1.5) respectively. Combine section numbers such as A - A’ and B - B’ to establish a spatial correspondence relationship, and sequentially draw the point position diagram to form a monitoring point position mapping diagram. Subsequently, according to the actual coordinate system, interpolate and draw the phase difference corresponding to the line segment between every two monitoring points. For example, divide the connection area between P1 and P2 into 3 equally spaced interpolation points with a step size of 0.5 m. According to the measured starting and ending phase values of 0° and 120° respectively, the phase values corresponding to the middle three interpolation points are 40°, 80°, and 120°. Thus, finely describe the phase change distribution between the monitoring points in the two-dimensional space and form a monitoring point position mapping diagram with corresponding spatial coordinates and phase differences.

[0020] S112: Based on the monitoring point position mapping diagram and the corresponding phase difference data, perform spatial interpolation processing in the continuous area, establish the phase change value distribution corresponding to each interpolation point, and obtain a two-dimensional phase change field. First, determine the coordinate values of the boundary nodes between the monitoring points with known phase differences and set the interpolation step size. Use the piecewise mean strategy to evenly divide the phase difference between adjacent boundary points to the intermediate interpolation nodes. For example, if 3 interpolation points are set between P1 and P2, with corresponding coordinates (0.5, 0), (1.0, 0), and (1.5, 0) respectively, and the boundary phase difference is 120°, then the phase values of each interpolation node are 40°, 80°, and 120° respectively. Then, perform bilinear interpolation on the non-boundary inner region, that is, at the four known point coordinates point 1: , point 2: , point 3: , point 4: The phase value of any point (x, y) within the rectangle formed is obtained by linearly weighted averaging the phase values of the surrounding points. Let the phase values of its four corners be , , , , and the distances from the (x, y) point are , , , , then the phase value is calculated as: ; where, : is the phase value of the interpolation point (x, y), : are the known phase values of the four corner points (unit: degree), : are the Euclidean distances between the interpolation point and each corner point (unit: meter), : are the interpolation weights defined by the reciprocal of the relative distance (dimensionless). Substituting gives: , , , , . This result is the phase value corresponding to the interpolation point in the phase field. The weight setting process is based on the principle of distance reciprocal to ensure that the points closer have greater weights, completing the construction of the entire two-dimensional phase change field.

[0021] S113: According to the phase change value along the direction of the diaphragm wall in the two-dimensional phase change field and the physical length between adjacent monitoring points in each observation section, calculate the ratio of the phase change value to the physical length to obtain the phase perturbation density index; According to the phase change value along the direction of the diaphragm wall in the two-dimensional phase change field and the physical length between adjacent monitoring points in each observation section, it is necessary to first select continuous observation sections arranged along the wall, such as sections A-A', B-B', etc., and measure the physical distances between pairwise monitoring points in each section. For example, the distance between monitoring points P1 and P2 in section A-A' is 1.5 m, and the distance between P2 and P3 is 1.6 m. Suppose their corresponding phase change values are 80° and 120° respectively. Define the ratio of the phase change value to the physical distance as the phase perturbation density index. , and its calculation is as follows: , where : represents the phase perturbation density index (unit: degree / meter), : is the phase change value between two points (unit: degree), : is the physical distance between two points (unit: meter).

[0022] Substitute specific data: , , and then set the classification standard threshold , to distinguish the perturbation levels, and the rules are set as follows: If , it is classified as low perturbation; if , it is classified as medium perturbation; if , it is high perturbation. This threshold is obtained from the data distribution of the average phase perturbation density per hour within 7 consecutive days. Take the 25th, 50th, and 75th percentiles as 45, 68, and 92 deg / m respectively. Accordingly, set 60 as the demarcation benchmark to ensure that the perturbation classification has the ability to distinguish. Finally, complete the calculation and zonal statistics of the phase perturbation density index, thereby forming a spatial distribution map of the perturbation density of the continuous observation section.

[0023] Please refer to Figure 3 , and the specific steps for obtaining the abnormal perturbation section set are as follows: S211: Call the coordinate data of the monitoring points, extract the geometric centers of the middle monitoring points of every three consecutive sections in the order of the observation section numbers, construct two direction vectors based on the adjacent geometric center coordinates, and calculate the change value of the included angle between the two direction vectors to obtain the spatial turning rate sequence; Call the coordinate data of the monitoring points, extract the geometric centers of the middle monitoring points of every three consecutive sections in the order of the observation section numbers. First, assume a series of observation sections arranged in order of numbers, namely A1, A2, A3...A n , and the corresponding middle monitoring points of the sections are marked as , where the three-dimensional coordinates of each point are , with the unit of meter. On this basis, for every three consecutive sections, such as section , section , section , the middle monitoring points are respectively , define the geometric center coordinates of these three points as: , where: : the coordinate value of the middle monitoring point of section in the three-dimensional coordinate system; : the coordinate of the middle monitoring point of section , which is the next one after section ; : the coordinate of the middle monitoring point of section , which is the second one after section ; : is the geometric center coordinate of sections , , , representing the central position of the spatial relationship of this group of three sections. For example, assume that the coordinates of the middle monitoring points of sections A1, A2, and A3 are , , respectively, then the corresponding geometric center can be calculated as: . Continue to slide the window and calculate the geometric center corresponding to the next section combination , and so on, to form a sequence of geometric centers. After that, construct the direction vectors between two adjacent geometric centers in turn, and define them as: , , where: : the geometric center of the current three sections; : from section to the geometric center; : from section to the geometric center; : from to the direction vector; : from to the direction vector. The calculation formula for the direction change angle is: , where: : is the dot product of the direction vectors, and the calculation method is , : is the modulus of the vector : , : is the modulus of the vector : , : represents the turning angle between three groups of three geometric centers, which is used to reflect the continuous change of direction. For example, assume , , , then the calculation of vector : From two geometric center points and to form a direction vector , and its calculation method is: . Substituting the numerical values gives: . The calculation of vector : From two geometric center points and to form a direction vector , and its calculation method is: . Substituting the numerical values gives: . The dot product is: . The modulus length is: , . Substitute: . Sequentially construct the included angle sequence of all cross-section groups to form a spatial return rate sequence for subsequent screening of abnormal area change points.

[0024] S212: Call the spatial return rate sequence and the phase perturbation density index corresponding to each observation cross-section, screen the observation cross-section numbers that meet the spatial return rate threshold and the perturbation density threshold, and obtain the double-threshold satisfied cross-section group; Call the spatial return rate sequence and the phase perturbation density index corresponding to each observation cross-section, and perform the double-condition screening operation in sequence. First, set the threshold of the spatial return rate to , and the threshold of the perturbation density to . Traverse all observation cross-section numbers , extract their spatial return rate values and perturbation density values one by one, and perform a numerical comparison operation. If both and are satisfied, then add the cross-section to the double-threshold satisfied set, and this set is denoted as , and its expression is: , where: : The included angle of the geometric centers of the three cross-sections where the cross-section is located, in degrees; : The phase perturbation density index of the cross-section , in units of ; : The set direction return rate threshold, with a value of , and this value is set based on the 75th percentile in the return angle distribution; : The set perturbation density threshold, with a value of , set by right-skewing the median of the overall perturbation density statistical distribution during the observation stage; : The set of section numbers that meet the above two conditions. If the space turnover rate of section A5 is , and the disturbance density is , and it meets and , then the number 5 is added to the set ; if for section A6 , although the disturbance density is higher than the threshold, it is not included because it does not meet the turnover rate condition. Finally, all sections that meet the conditions form the set , which is used as the candidate sample for subsequent spatial clustering.

[0025] S213: According to the spatial distribution density of each section in the section group that meets the double threshold, perform spatial distribution density peak clustering operation, classify and aggregate the section numbers to obtain the abnormal disturbance section set; According to the spatial distribution density of each section in the section group that meets the double threshold, perform density peak clustering operation, adopt the density - distance combined criterion to identify the abnormal clustering center. First, for each section , calculate its spatial density value and the minimum high - density distance , where the density value is defined as follows: , where: : is the spatial proximity density of section , representing the number of neighbors within the cut - off distance ; : is the geometric center distance between section and section , in meters, : is the cut - off distance for density calculation. In this embodiment, it takes , : is the indicator function, defined as , for example, if the distances from section to the other 5 sections are 3.2m, 6.1m, 2.8m, 4.5m, 5.5m respectively, and the number of neighbors that meet is 3, so . Next, calculate the geometric center distance from section to all sections that meet , and take the minimum value as : , for example, if the distances between section and three sections with higher density are 2.1m, 3.3m, 6.4m respectively, then . Finally, obtain the binary index points corresponding to all sections , which form a scatter plot in the two - dimensional plane. Select high and The large points are used as clustering centers, and this point is defined as the local density peak. The remaining cross-sections are assigned to the corresponding clusters according to the minimum Euclidean distance from the clustering centers, and the following assignment is performed: calculate the distances from the current cross-section to all clustering centers, and take the minimum one, which is set as the belonging class; If a cross-section is equidistant from multiple centers, it is preferentially assigned to the center with the highest density; repeat the execution until all cross-sections are assigned. This clustering operation does not require manual specification of the number of clusters, and automatically infers the centers through density and relative distance, and is applicable to the scenario of identifying structural deformation abnormal space clusters.

[0026] Please refer to Figure 4 , and the specific steps for obtaining the set of abnormal response cross-sections at the tail section are as follows: S311: Obtain the vibration signals recorded by the acceleration sensors on the underground wall, identify the termination point of the main frequency energy attenuation, and extract the tail-section vibration signals backward from the termination point. Respectively, count the tail-section duration and the ratio of the tail-section vibration energy to the total energy to obtain the vibration tail-section feature vector sequence; Obtain the vibration signals recorded by the acceleration sensors on the underground wall. First, arrange the acceleration sensor nodes on the underground continuous wall. The sensor arrangement spacing is set to 3 meters, the sampling frequency is 1000 Hz, and the collected signal is the time-domain acceleration value sequence, denoted as , where is the time variable (unit: second). Convert this signal into a frequency-domain signal through Fourier transform , is the frequency (unit: Hz). Extract the main frequency of energy and its corresponding power amplitude . Calibrate the energy attenuation process corresponding to the main frequency response in the time domain. Let the initial main frequency amplitude be . When the main frequency energy decays to the set termination ratio , the termination power threshold is , and the corresponding time point is recorded as the termination moment . The tail-section vibration signal is intercepted backward from to the end of the signal, and the length is the tail-section duration , with the unit of millisecond (ms). Let the total energy of the signal be , the tail-section energy be , and the ratio of the tail-section energy be , where is the signal termination moment. If the signal recorded by a certain sensor lasts for 2 seconds, the total energy is 14 J, the main frequency energy attenuation terminates at 1.65 seconds, and the tail-section energy is 2.8 J, then: the tail-section duration , the ratio of the tail-section energy . Represent the tail-section feature of this sensor as a two-dimensional vector , where the subscript Indicates the current cross-section number The first and second dimensions of the corresponding eigenvector are the tail section duration and the tail section energy ratio respectively, and finally a vibration tail section eigenvector sequence arranged in the order of cross-section numbers is formed.

[0027] S312: According to the vibration tail section eigenvector sequence, arrange the vector sequence in the order of the cross-section numbers along the diaphragm wall, and calculate the Mahalanobis distance based on the tail section eigenvectors of each pair of adjacent cross-sections to obtain the vibration difference degree sequence between cross-sections; According to the vibration tail section eigenvector sequence, arrange them in turn according to the diaphragm wall cross-section numbers as , where each vector , represents the cross-section 's tail section duration and the tail section energy ratio , with units of milliseconds (ms) and dimensionless ratio respectively. Then, for each pair of adjacent cross-section numbers and calculate the Mahalanobis distance of the eigenvectors . Among them: : is the tail section eigenvector of the th cross-section, : is the tail section duration (unit: ms) of the cross-section , : is the tail section energy ratio (unit: dimensionless) of the cross-section , : is the tail section eigenvector of the th cross-section, , : are the tail section duration and energy ratio of the cross-section respectively; : is the difference vector between the two eigenvectors; : is the inverse matrix of the covariance matrix of the entire set of vibration tail section eigenvectors, where is a two-dimensional matrix , among which, : represents the variance of the tail section duration , that is, the degree of dispersion of the tail section duration among all cross-sections, with the unit of . : represents the variance of the tail section energy ratio , that is, the degree of dispersion of the tail section energy ratio among all cross-sections, with the unit of dimensionless squared. : represents the covariance between the tail section duration and the tail section energy ratio , that is, the degree of linear correlation between the two, with the unit of ms dimensionless. : and Same. Since the covariance matrix is symmetric, these two values are equal. For example, assume a cross-section , there is , cross-section , there is . If the full-sample covariance matrix is: , where is the element in the th row and th column of the inverse matrix (obtained through the method of finding the inverse of the covariance matrix), the difference vector is . Substitute it into the Mahalanobis distance expression, perform matrix multiplication and square root to obtain a certain value, representing the distance measure of the tail section characteristics between cross-section 1 and 2. Calculate the distance between each pair of adjacent cross-section numbers in this way to construct a complete sequence of vibration difference degrees between cross-sections.

[0028] S313: Call the sequence of vibration difference degrees between cross-sections, and combine the proportion of residual energy in the tail section and the change trend of the tail section duration to screen the cross-section numbers to obtain the set of cross-sections with abnormal tail section responses; Call the sequence of vibration difference degrees between cross-sections , and combine the energy ratio in the tail section and the change trend of the duration to screen the cross-sections. First, set the Mahalanobis distance screening threshold . If a pair of cross-sections satisfies , it is regarded as a significant difference in the vibration performance of the tail section. Further analyze the change of the tail section duration and energy ratio between cross-section and its adjacent front and rear cross-sections . Define: , : The absolute difference in the tail section duration from the adjacent cross-section; , : The difference in the energy ratio from the adjacent cross-section; If any , , where , are the set thresholds, then it is considered that a mutation in the tail section response occurs at this cross-section; The final screening condition is that cross-section satisfies and any exceeds the set threshold. For example, cross-section A4 has a tail section feature vector , and its front and rear are , respectively. Then the differences are: , . If , then cross-section A4 is marked as an abnormal response cross-section. Finally, all the cross-section numbers that satisfy the mutation of vibration difference degree and the mutation of eigenvector are classified into the abnormal response cross-section set at the tail section, which is used for the structural safety state assessment and the warning calibration of the risk section.

[0029] Please refer to Figure 5 , the specific steps for obtaining the key monitoring cross-section set are as follows: S411: Based on the cross-section number data of the abnormal disturbance cross-section set and the abnormal response cross-section set at the tail section, extract the cross-section number sequence that exists in both sets simultaneously to generate an intersection cross-section number set; First, read each cross-section number in the two number sets in sequence. Let the number set of the abnormal disturbance cross-section set be set , and the number set of the abnormal response cross-section set at the tail section be set . Compare the two sets one by one with the cross-section number as the unit. During the execution process, for each cross-section number in set , perform a search operation to determine whether it exists in set . If the judgment result is true, that is, the condition is satisfied, then add the current number to the intersection result set . Finally, obtain the intersection set . For example, if number 5 satisfies the condition of existing in both set A and set B, then . Similarly, if number 9 also satisfies the condition, the finally formed set is , where the letter represents the -th cross-section number in set , the letter represents the number set of the abnormal response cross-section set at the tail section, the letter represents the finally extracted intersection cross-section set, and the letter represents the sequence index value in the set. Through this process, the cross-section numbers with both abnormal disturbance characteristics and tail section response characteristics are extracted.

[0030] S412: According to the intersection cross-section number set, extract the phase perturbation density index, spatial return rate, tail section duration, and energy residue ratio corresponding to each cross-section to obtain a cross-section joint eigenvector sequence; According to the intersection cross-section number set , call the multi-source data record, and extract the phase perturbation density index, spatial return rate, tail section duration, and energy residue ratio corresponding to each cross-section number one by one. When the cross-section number is 5, the recorded data is the phase perturbation density index , spatial return rate , tail section duration , and the energy residue ratio of the tail section When the cross-section number is 9, the corresponding recorded data is , , , , where the letter represents the phase perturbation density index of cross-section , with the unit of degrees per meter (deg / m). The letter represents the spatial return rate of cross-section , with the unit of angle. The letter represents the vibration duration of the tail section of cross-section , with the unit of milliseconds (ms). The letter represents the proportion of the remaining energy in the tail section of cross-section , which is a dimensionless value representing the ratio of the tail section energy to the total energy. During the data standardization process, let the maximum value of the phase perturbation density be , and the minimum value be . Then the normalization result of cross-section 5 is . The same operation is performed on the other three items in turn, and all the data are uniformly mapped between 0 and 1 to complete the standardized vector combination and construct the joint feature vector , where the letter represents the joint feature vector corresponding to the cross-section number , which is used as the input data in the subsequent classification analysis.

[0031] S413: Perform unsupervised classification on the sequence of cross-section joint feature vectors, screen the cross-section numbers with a feature distribution concentration higher than the concentration threshold, and obtain the key monitoring cross-section set; Perform unsupervised classification on the constructed sequence of joint feature vectors . First, perform the operations of calculating the feature mean and standard deviation, and perform statistical operations on each of the four features. Let the two data in the tail section duration vector be , . Then the mean calculation result is , and the standard deviation is . Similarly, for the proportion of remaining energy, it is , . Its mean is , and the standard deviation is . Then perform feature distance analysis, calculate the Euclidean distance between the normalized vectors of cross-section numbers 5 and 9. Let the standardized vectors be , . Their Euclidean distance is: . Set the distance concentration threshold to . Since , so cross-section numbers 5 and 9 are jointly classified into the set of key monitoring cross-sections, and finally the set of main monitoring cross-section numbers obtained is , where the letter respectively represent the mean values of the tail section duration and the proportion of energy residue, and the letter respectively represent the corresponding standard deviations, and the letter represents the cross-section number is the joint feature vector after normalization, and the letter represents the Euclidean distance between two vectors, and the letter is the distance threshold for centralized judgment. Through the above process, the judgment of the concentration degree of feature distribution is completed and the set of key monitoring cross-sections is output.

[0032] Please refer to Figure 6 , and the specific steps for obtaining the risk sections of construction disturbance monitoring are as follows: S511: Obtain the disturbance start time and the starting time of the vibration tail section of each monitoring point in the set of key monitoring cross-sections. Perform time-axis alignment processing on the two types of time series in sequence according to the cross-section number order, calculate the difference between the disturbance time and the response time of each group of cross-sections, and generate a cross-section disturbance-response time difference sequence; Obtain the disturbance start time and the starting time of the vibration tail section of each monitoring point in the set of key monitoring cross-sections. Sort them in ascending order according to the cross-section number, and read the time data recorded by the monitoring points within each cross-section number. Assume the cross-section numbers are 3, 5, 7, 9, 11, and their corresponding disturbance start times are 13.52 seconds, 14.75 seconds, 16.03 seconds, 17.21 seconds, 18.88 seconds respectively, and the corresponding starting times of the tail sections are 14.01 seconds, 15.18 seconds, 16.59 seconds, 17.93 seconds, 19.65 seconds respectively. Then perform time-axis alignment processing on the two types of time series, and use the absolute time reference system for unified positioning, that is, keep all times as real time points without relative offset. Then calculate the difference between the disturbance time and the starting time of the tail section within each cross-section number in sequence, and perform it using the standard subtraction method. The time difference of cross-section 3 is 14.01 seconds minus 13.52 seconds, and the calculation result is 0.49 seconds. The time difference of cross-section 5 is 15.18 seconds minus 14.75 seconds, and the calculation process is 15.18 - 14.75 = 0.43 seconds. The time difference of cross-section 7 is 16.59 - 16.03 = 0.56 seconds. For cross-section 9, it is 17.93 - 17.21 = 0.72 seconds. For cross-section 11, it is 19.65 - 18.88 = 0.77 seconds. Thus, the complete disturbance-response time difference sequence obtained is 0.49 seconds, 0.43 seconds, 0.56 seconds, 0.72 seconds, and 0.77 seconds. This sequence represents the time delay value from the start of disturbance to the start of tail section response within each cross-section, forming a unified and ordered set of response lag time data.

[0033] S512: Call the cross-section disturbance-response time difference sequence and the two types of original time series, and calculate the maximum mutual information value, average mutual information value, and maximum time difference for each continuous path segment to obtain the path segment information interdependence parameter set; Call the cross-section disturbance-response time difference sequence and its corresponding disturbance time sequence and tail-section response time sequence, and analyze the path segments formed by all adjacent cross-section numbers. Suppose the cross-section numbers are 3, 5, 7, 9, 11, then the continuous path segments are 3-5, 5-7, 7-9, 9-11 in sequence. Each path segment consists of two cross-sections. Extract the disturbance start time and tail-section response start time data of the monitoring points of the two cross-sections contained in each path segment, and respectively form the disturbance time sequence and the response time sequence , where represents the time data sequence in the disturbance stage, with the unit of seconds (s), represents the time data sequence in the tail-section response stage, with the same unit of seconds. For the convenience of consistency analysis, resample each sequence with a sampling period of 0.01 seconds so that it contains 100 sampling points, forming two time series sets and . For each path segment, use the definition of mutual information in information theory to calculate the mutual information value between the disturbance time sequence and the response time sequence . Its calculation formula is: , where: : represents the mutual information value between the disturbance time sequence and the response time sequence , with the unit of bits (bit), indicating their statistical correlation; : represents the joint probability that the disturbance time value and the response time value appear simultaneously; : the marginal probability that the disturbance time value appears; : the marginal probability that the response time value appears; : the logarithmic function with base 2, used to convert the probability ratio into information quantity (bit). The calculation process includes the following steps: First, count all possible values in the disturbance sequence and the response sequence to form a discrete value set. For each value pair , count the frequency of their simultaneous appearance in the sample, divide by the total number of samples to obtain the joint probability , and then count the frequency of appearing in all samples to get , and similarly get , then substitute all joint probability terms and marginal probability terms into the above formula, perform summation, and finally obtain the mutual information between the perturbation and the response within the path segment. Taking path segment 5-7 as an example, assume the perturbation time series is , and the response time series is . There are 100 sampling points for both. It is statistically known that the perturbation time 14.75 and the response time 15.18 appear simultaneously 4 times in the sample. Then , the perturbation time 14.75 appears a total of 5 times. Then , the response time 15.18 appears 6 times. Then . Substitute into the formula to get: , this is only the contribution value of one 、 pairing item in the mutual information formula. Other pairing items are solved in the same way, and finally the complete mutual information value is obtained by summation . For example, if the calculation result is 0.64, it means that the perturbation and response of the path segment are highly correlated; further, the average of the mutual information of all paired values is used as the average mutual information, and assume it is 0.52; then extract the maximum time difference between the two cross-sections in the path segment as 0.85 seconds. Finally, the information interdependence parameter group formed by this path segment is , indicating its perturbation-response correlation and response delay characteristics. Repeat the above operations for all path segments to form a complete set of information interdependence parameters.

[0034] S513: According to the mutual information value and the maximum time difference of each path segment in the path segment information interdependence parameter set, perform the section grading judgment operation to obtain the risk section of the construction perturbation monitoring and carry out response prevention and control warnings; According to the set of mutually dependent parameters of the path segment information, perform a hierarchical judgment operation on all path segments, and read the three parameter values of the maximum mutual information value, average mutual information value, and maximum time difference segment by segment. First, set the mutual information value level interval. If the maximum mutual information value is less than 0.2, it is regarded as a low mutual dependence section. Between 0.2 and 0.5 is the medium mutual dependence section, and higher than 0.5 is the high mutual dependence section. Then set the maximum time difference level limit. If the time difference is less than or equal to 0.4 seconds, it is a low delay section. From 0.4 seconds to 0.7 seconds is the medium delay section, and greater than 0.7 seconds is the high delay section. For each path segment, compare the mutual information value and time difference with the above level intervals respectively, and perform a segment-by-segment judgment and classification operation. For example, the maximum mutual information value of path segment 5-7 is 0.64, belonging to the high mutual dependence section, and the maximum time difference is 0.56 seconds, belonging to the medium delay section. Then this path segment is classified into the "high mutual dependence medium delay" section and marked as a secondary risk; the maximum mutual information value of path segment 7-9 is 0.41, and the maximum time difference is 0.72 seconds, corresponding to "medium mutual dependence high delay", which is also classified as a secondary risk; the maximum mutual information value of path segment 9-11 is 0.68, and the time difference is 0.77 seconds, both of which are high-level items. This path segment is classified into the primary risk section. After completing the classification operation, the numbers of all path segments classified as primary and secondary risk levels are integrated into the construction disturbance monitoring risk section set. The monitoring path segments corresponding to each risk section number will be set with response prevention and control identifiers and uploaded to the alarm subsystem to generate an alarm signal and link the log recording system to generate risk section codes, risk levels, and the first alarm timestamp information, completing the final response output process.

[0035] A monitoring and prevention and control system for road construction processes, the system includes: The phase density calculation module monitors the distance phase change between monitoring points around the underground structure and the position of the observation section during the construction of the road foundation pit through laser ranging, performs spatial distribution fitting processing, and calculates the phase disturbance density index of each observation section; The turning-back rate clustering module calls the coordinate data of the monitoring points, constructs the center line vector of the continuous observation section position, calculates the angle change between the vectors as the spatial turning-back rate, and performs density peak clustering on the phase disturbance density index of each observation section and the spatial turning-back rate of the corresponding observation section to obtain the abnormal disturbance section set; The vibration tail section extraction module obtains the vibration signal recorded by the underground wall acceleration sensor, extracts the duration and energy residue ratio of the signal wave tail part, judges the characteristic difference degree between adjacent sections, and obtains the tail section abnormal response section set; The abnormal intersection recognition module performs cross-classification recognition on the abnormal disturbance section set and the tail section abnormal response section set to screen the key monitoring section set; The risk calibration and warning module obtains the disturbance start time and the starting time of the vibration tail section of the monitoring points in the key monitoring section set, conducts an information interdependence analysis on the time synchronization relationship, calibrates the risk section of the construction disturbance monitoring, and conducts response prevention and control warnings.

[0036] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as the technical content of the technical solution of the present invention is not departed from, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for monitoring, preventing and controlling the road construction process, characterized in that It includes the following steps: S1: Monitor the distance phase change between monitoring points around the underground structure and the position of the observation section during the construction of the road foundation pit by laser ranging, perform spatial distribution fitting processing, and calculate the phase perturbation density index of each observation section; S2: Call the coordinate data of the monitoring points, construct the center line vector of the continuous observation section position, calculate the angle change between the vectors as the spatial turning rate, perform density peak clustering on the phase perturbation density index of each observation section and the spatial turning rate of the corresponding observation section, and obtain the abnormal perturbation section set; S3: Obtain the vibration signal recorded by the underground wall acceleration sensor, extract the duration of the signal tail part and the proportion of energy residue, judge the characteristic difference degree between adjacent sections, and obtain the tail section abnormal response section set; S4: Perform cross-classification identification on the abnormal perturbation section set and the tail section abnormal response section set, and screen the key monitoring section set; S5: Obtain the disturbance start time and the starting time of the vibration tail section of the monitoring points in the key monitoring section set, perform information interdependence analysis on the time synchronization relationship, calibrate the risk section of the construction disturbance monitoring, and perform response prevention and control warnings.

2. The method for monitoring, preventing and controlling the road construction process according to claim 1, characterized in that, The phase perturbation density index includes the disturbance gradient distribution value, the disturbance intensity of a specified length, and the observation section coordinate sequence. The abnormal perturbation section set is specifically the abnormal section number, the section spatial offset direction, and the section aggregation density coefficient. The tail section abnormal response section set includes the energy residue index, the wave tail duration, and the vibration difference degree between sections. The key monitoring section set specifically refers to the section risk ranking label, the four-dimensional feature combination value, and the section identification stability level. The risk section of the construction disturbance monitoring includes the first-level risk section, the second-level risk section, and the early warning attention section.

3. The method for monitoring and preventing road construction process according to claim 1, wherein, The specific steps for obtaining the phase perturbation density index are as follows: S111: Obtain the laser ranging phase difference between the monitoring points around the underground structure during the construction of the road foundation pit and the position of the observation section corresponding to the monitoring points, construct a two-dimensional coordinate distribution area based on the coordinate relationship of the monitoring points, and generate a monitoring point position mapping diagram; S112: Based on the monitoring point position mapping diagram and the corresponding phase difference data, perform spatial interpolation processing in the continuous area, establish the phase change value distribution corresponding to each interpolation point, and obtain a two-dimensional phase change field; S113: According to the phase change value along the direction of the underground continuous wall in the two-dimensional phase change field and the physical length between adjacent monitoring points in each observation section, calculate the ratio of the phase change value to the physical length to obtain the phase perturbation density index.

4. The method for monitoring, preventing and controlling during the road construction process according to claim 3, wherein, The specific steps for obtaining the abnormal perturbation section set are as follows: S211: Call the coordinate data of the monitoring points, extract the geometric center of the middle monitoring points of every three consecutive sections in the order of the observation section numbers, construct two direction vectors based on the adjacent geometric center coordinates, and calculate the angle change value of the two direction vectors to obtain the spatial turning rate sequence; S212: Call the spatial turning rate sequence and the phase perturbation density index corresponding to each observation section, screen the observation section numbers that meet the spatial turning rate threshold and the perturbation density threshold, and obtain the double-threshold satisfaction section group; S213: Perform a spatial distribution density peak clustering operation according to the spatial distribution density of each cross-section in the double-threshold satisfaction cross-section group, classify and aggregate the cross-section numbers, and obtain a set of abnormally disturbed cross-sections.

5. The method for monitoring and preventing road construction process according to claim 4, characterized in that, The specific steps for obtaining the set of abnormally responsive cross-sections in the tail section are as follows: S311: Obtain the vibration signals recorded by the underground wall acceleration sensors, identify the termination point of the main frequency energy attenuation, extract the tail-section vibration signals backward from the termination point, and respectively count the tail-section duration and the ratio of the tail-section vibration energy to the total energy to obtain a sequence of vibration tail-section feature vectors; S312: According to the sequence of vibration tail-section feature vectors, arrange the vector sequence in the order of the cross-section numbers along the underground continuous wall, calculate the Mahalanobis distance based on the tail-section feature vectors of each pair of adjacent cross-sections, and obtain a sequence of vibration differences between cross-sections; S313: Call the sequence of vibration differences between cross-sections, and combine the ratio of the remaining tail-section energy and the change trend of the tail-section duration to screen the cross-section numbers to obtain a set of abnormally responsive cross-sections in the tail section.

6. The method for monitoring and preventing road construction process according to claim 5, characterized in that, The specific steps for obtaining the set of key monitoring cross-sections are as follows: S411: Based on the cross-section number data of the abnormally disturbed cross-section set and the set of abnormally responsive cross-sections in the tail section, extract the sequence of cross-section numbers that exist in both sets to generate an intersection set of cross-section numbers; S412: According to the intersection set of cross-section numbers, extract the phase disturbance density index, spatial turning rate, tail-section duration, and the ratio of the remaining energy corresponding to each cross-section to obtain a sequence of cross-section combined feature vectors; S413: Perform unsupervised classification processing on the sequence of cross-section combined feature vectors, screen the cross-section numbers with a feature distribution concentration higher than the concentration threshold, and obtain a set of key monitoring cross-sections.

7. The method for monitoring and preventing road construction process according to claim 6, wherein The specific steps for obtaining the risk sections of the construction disturbance monitoring are as follows: S511: Obtain the disturbance start time and the starting time of the vibration tail section of each monitoring point in the set of key monitoring cross-sections, perform time-axis alignment processing on the two types of time series in sequence according to the cross-section number order, calculate the difference between the disturbance time and the response time of each group of cross-sections, and generate a sequence of cross-section disturbance-response time differences; S512: Call the sequence of cross-section disturbance-response time differences and the two types of original time series, calculate the maximum mutual information value, average mutual information value, and maximum time difference for each continuous path segment to obtain a set of path segment information interdependence parameters; S513: According to the mutual information value and the maximum time difference of each path segment in the set of path segment information interdependence parameters, perform a section grading judgment operation to obtain the risk sections of the construction disturbance monitoring and issue a response prevention and control warning.

8. A road construction process monitoring and prevention and control system, characterized in that, According to the method for monitoring and prevention and control during the road construction process according to any one of claims 1-7, the system includes: The phase density calculation module monitors the distance phase change and the observation cross-section position between the monitoring points around the underground structure during the road foundation pit construction process through laser ranging, performs spatial distribution fitting processing, and calculates the phase disturbance density index of each observation cross-section; The turning-back rate clustering module calls the coordinate data of the monitoring points, constructs the center line vector of the continuous observation section position, calculates the change in the angle between the vectors as the spatial turning-back rate, and performs density peak clustering on the phase perturbation density index of each observation section and the spatial turning-back rate of the corresponding observation section to obtain a set of abnormally perturbed sections; The vibration tail section extraction module obtains the vibration signals recorded by the underground wall acceleration sensors, extracts the duration and the proportion of energy residue in the tail part of the signal wave, and judges the characteristic difference degree between adjacent sections to obtain a set of tail section abnormally responsive sections; The abnormal intersection identification module performs cross-classification identification on the set of abnormally perturbed sections and the set of tail section abnormally responsive sections to screen out a set of key monitoring sections; The risk calibration and warning module obtains the disturbance start time and the vibration tail section start time of the monitoring points in the set of key monitoring sections, performs information interdependence analysis on the time synchronization relationship, calibrates the risk sections of the construction disturbance monitoring, and performs response prevention and control warnings.

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