State Monitoring Method, Device, Medium and Program Product for Construction Climbing Scaffold

Abstract

An embodiment of the present application provides a method, device, medium, and program product for monitoring the state of a building climbing formwork. The method includes: obtaining satellite positioning monitoring data of the climbing formwork; identifying the deformation condition of the target climbing formwork, and combining the deformation condition to perform gross error rejection on the satellite positioning monitoring data; performing data interpolation on the data after gross error rejection; based on the discrete model of the observation system, combining the robust Kalman filter model to perform filtering processing on the preprocessed data; based on the filtered data, real-time identifying the current motion state of the target climbing formwork, and based on the current motion state, performing abnormal state monitoring on the target climbing formwork based on the filtered data. By obtaining the monitoring data of the climbing formwork based on satellite positioning technology and preprocessing the monitoring data through gross error rejection, data interpolation, and robust Kalman filtering, etc., the embodiment of the present application monitors the state of the climbing formwork dynamically, effectively improving the comprehensiveness and reliability of the climbing formwork state monitoring.

Description

Technical Field

[0001] The present application relates to the technical field of climbing frame monitoring. Specifically, it relates to a method, device, medium and program product for monitoring the state of a building climbing frame. Background Art

[0002] With the acceleration of the urbanization process and the continuous development of modern construction technologies, the number of super high-rise buildings is increasing continuously. During the high-altitude construction process, as a key supporting device, the safety of the external climbing frame directly affects the smooth progress of construction and the project quality. In the complex environment of the city center, the stability and operating state of the external climbing frame are easily affected by various external factors such as wind load, vibration, and temperature change. Therefore, long-term real-time monitoring of the external climbing frame, especially the dynamic feature tracking during the climbing process, is of great significance for ensuring construction safety, evaluating the structural health status, and implementing safety warnings.

[0003] Currently, regarding the monitoring problem of the external climbing frame in building construction, on the one hand, existing research mainly focuses on the monitoring of the structural state of the climbing frame itself. On the other hand, the influence of external loads on the climbing frame has also received much attention. Although the existing monitoring schemes have improved the efficiency and automation level of climbing frame safety monitoring to a certain extent, these schemes often only focus on one-sided aspects and cannot comprehensively, accurately and reliably achieve dynamic monitoring of the climbing frame state. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a method, device, medium and program product for monitoring the state of a building climbing frame, so as to improve the comprehensiveness and reliability of climbing frame state monitoring.

[0005] In a first aspect, the embodiments of the present application provide a method for monitoring the state of a building climbing frame, including:

[0006] Obtaining satellite positioning monitoring data of a target climbing frame;

[0007] Taking a preset data volume as a processing window, dynamically identifying the deformation condition of the target climbing frame within the current processing window based on the satellite positioning monitoring data, and combining the deformation condition to perform gross error rejection on the satellite positioning monitoring data of the current processing window;

[0008] Performing data interpolation on the satellite positioning monitoring data after gross error rejection to obtain preprocessed data;

[0009] Based on the pre-constructed discrete model of the observation system corresponding to the target climbing frame, combining with a preset robust Kalman filter model to perform filtering processing on the preprocessed data to obtain filtered data;

[0010] Based on the filtered data, the current motion state of the target climbing frame is recognized in real time, and based on the monitoring rules corresponding to the current motion state, the target climbing frame is monitored for abnormal states based on the filtered data.

[0011] In the embodiments of the present application, by obtaining the monitoring data of the climbing frame based on satellite positioning technology, and preprocessing the monitoring data through gross error rejection, data interpolation, robust Kalman filtering, etc., finally, by recognizing the motion state of the climbing frame and based on different rules to monitor the state of the climbing frame dynamically, the comprehensiveness and reliability of the climbing frame state monitoring are effectively improved.

[0012] In some possible embodiments, using the preset data volume as a processing window, the deformation condition of the target climbing frame within the current processing window is dynamically recognized based on the satellite positioning monitoring data, and gross error rejection is performed on the satellite positioning monitoring data of the current processing window in combination with the deformation condition, including:

[0013] Using the preset data volume as a processing window, based on the satellite positioning monitoring data, the confidence interval corresponding to the current processing window is determined using the 3-sigma rule;

[0014] Based on the confidence interval, it is judged whether the target climbing frame deforms within the current processing window;

[0015] In the case where the target climbing frame does not deform, gross error rejection is performed on the satellite positioning monitoring data of the current processing window based on the confidence interval;

[0016] In the case where the target climbing frame deforms, the confidence interval is adjusted, and gross error rejection is performed on the satellite positioning monitoring data of the current processing window based on the adjusted confidence interval.

[0017] In the embodiments of the present application, by recognizing the deformation condition of the climbing frame and excluding the influence of the deformation condition on the gross error rejection process, the reliability of the monitoring data preprocessing is further improved.

[0018] In some possible embodiments, the judging whether the target climbing frame deforms within the current processing window based on the confidence interval includes:

[0019] Based on the confidence interval, the gross error data within the current processing window is recognized from the satellite positioning monitoring data, and the corresponding gross error data volume is counted;

[0020] Based on whether the gross error data volume exceeds the preset data volume threshold, it is determined whether the target climbing frame deforms within the current processing window.

[0021] In the embodiments of the present application, by counting the amount of gross error data in each processing window as the basis for determining whether the climbing frame is deformed, the reliability of the preprocessing of the monitoring data is further improved.

[0022] In some possible embodiments, based on the pre-constructed discrete observation system model corresponding to the target climbing frame, and in combination with a preset robust Kalman filter model, filtering is performed on the preprocessed data to obtain filtered data, including:

[0023] Obtain the pre-constructed discrete observation system model corresponding to the target climbing frame, construct an observation covariance matrix based on the IGG3 equivalent weight function, and use the observation covariance matrix to replace the observation noise covariance matrix of the traditional Kalman filter model to obtain a robust Kalman filter model;

[0024] In combination with the discrete observation system model and the preset robust Kalman filter model, perform filtering on the preprocessed data to obtain filtered data.

[0025] In the embodiments of the present application, by using the IGG3 equivalent weight function for robust estimation, the filtering process can better handle the noise and errors in the observation data, thereby effectively improving the accuracy of data filtering.

[0026] In some possible embodiments, based on the pre-constructed discrete observation system model corresponding to the target climbing frame, and in combination with a preset robust Kalman filter model, filtering is performed on the preprocessed data to obtain filtered data, including:

[0027] During the prediction and update process of the robust Kalman filter model, perform height constraint on the predicted value of each iteration based on a preset height constraint condition;

[0028] Among them, within a preset initial monitoring period, a preset height threshold is used as the height constraint condition; within the remaining monitoring periods other than the initial monitoring period, a corresponding height constraint condition is calculated in real time based on a preset angle threshold.

[0029] In the embodiments of the present application, by using the height direction constraint condition to weaken the short multipath effect in the data monitoring process, the accuracy of the dynamic monitoring of the positioning data is further optimized.

[0030] In some possible embodiments, the real-time identification of the current motion state of the target climbing frame based on the filtered data includes:

[0031] Based on the filtered data, the average value of the corresponding sequence points in each window is calculated in real time by using a preset sliding reference window and a sliding detection window; wherein, the sliding reference window and the sliding detection window are configured with a preset overlapping length;

[0032] The difference between the average values of the sequence points of the sliding reference window and the sliding detection window is calculated in real time, and the current motion state of the target climbing frame is determined based on the magnitude relationship between the difference within a preset time period and a preset threshold.

[0033] In the embodiment of the present application, by obtaining the characteristic quantity for judging the motion state of the climbing frame based on the sliding detection window and the reference window, the accuracy of identifying the motion state of the climbing frame is further improved.

[0034] In some possible embodiments, the abnormal state monitoring of the target climbing frame based on the filtered data according to the monitoring rule corresponding to the current motion state includes:

[0035] Based on the filtered data, the offset statistic of the sequence data relative to a preset reference value is statistically calculated in real time by using the CUSUM algorithm;

[0036] According to whether the offset statistic exceeds a preset warning threshold, it is determined whether the target climbing frame is in an abnormal state; wherein, the warning threshold is determined based on the monitoring rule corresponding to the current motion state.

[0037] In the embodiment of the present application, by statistically calculating the cumulative sum of the offsets of the sequence data based on the CUSUM algorithm and using it as the basis for judging whether the climbing frame is in an abnormal state, the accuracy of the climbing frame state monitoring is further improved.

[0038] In a second aspect, the embodiment of the present application provides a state monitoring device for a building climbing frame, including:

[0039] A data acquisition module, configured to acquire satellite positioning monitoring data of a target climbing frame;

[0040] A gross error rejection module, configured to use a preset data volume as a processing window, dynamically identify the deformation condition of the target climbing frame within the current processing window based on the satellite positioning monitoring data, and combine the deformation condition to reject the gross error of the satellite positioning monitoring data within the current processing window;

[0041] A data interpolation module, configured to perform data interpolation on the satellite positioning monitoring data after gross error rejection to obtain preprocessed data;

[0042] A data filtering module, configured to perform filtering processing on the preprocessed data based on the discrete model of the observation system corresponding to the target climbing frame constructed in advance and in combination with a preset robust Kalman filtering model to obtain filtered data;

[0043] A status monitoring module, configured to identify the current motion state of the target climbing formwork in real time based on the filtered data, and perform abnormal state monitoring on the target climbing formwork based on the filtered data according to the monitoring rules corresponding to the current motion state.

[0044] In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in any embodiment of the first aspect can be implemented.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the method described in any embodiment of the first aspect can be implemented.

[0046] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method described in any embodiment of the first aspect can be implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0048] Figure 1 It is a schematic flowchart of a method for monitoring the status of a building climbing formwork provided by an embodiment of the present application;

[0049] Figure 2 It is a schematic overall flowchart of a method for monitoring the status of a building climbing formwork provided by an embodiment of the present application;

[0050] Figure 3 It is a schematic diagram of a dual-sliding window event detection model provided by an embodiment of the present application;

[0051] Figure 4 It is a schematic structural diagram of a device for monitoring the status of a building climbing formwork provided by an embodiment of the present application;

[0052] Figure 5 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] Next, the technical solutions in the embodiments of the present application will be described with reference to the accompanying drawings in the embodiments of the present application.

[0054] It should be noted that: Similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first" and "second" are only used for differential description and cannot be construed as indicating or implying relative importance.

[0055] As Figure 1 shown, the embodiment of the present application provides a method for monitoring the state of a building climbing formwork, which mainly includes the following steps:

[0056] S1. Obtain the satellite positioning monitoring data of the target climbing formwork.

[0057] First, GNSS (Global Navigation Satellite System) integrated receivers are deployed at key positions (such as four corner positions, etc.) on the building climbing formwork. At the same time, a reference station is set up outside the building, and four-system satellite signals are obtained through dual-frequency observation, including GPS (Global Positioning System) data, Galileo data, and BDS (Beidou Satellite Navigation System) data, etc. These original monitoring data can be transmitted to the data processing center in real time at an epoch interval of 1 second, and after appropriate preprocessing operations, the data of a certain key point is generated as the satellite positioning monitoring data of the target climbing formwork.

[0058] S2. Use a preset data volume as the processing window, dynamically identify the deformation condition of the target climbing formwork within the current processing window based on the satellite positioning monitoring data, and combine the deformation condition to eliminate gross errors in the satellite positioning monitoring data of the current processing window.

[0059] It should be noted that in the monitoring of the external climbing scaffold of super high-rise buildings, due to this scenario being in a typical urban canyon area, the occlusion and reflection of buildings cause non-line-of-sight (NLOS) errors and multipath effects in GNSS signals. At the same time, the number of visible satellites decreases, and the geometric layout of the visible satellites is worse than that in an open environment. This leads to problems such as the ambiguity not being fixed at certain moments during the real-time solution process, and there is a relatively serious drift phenomenon in the solution results. Since these original monitoring data may have a particularly large gap from the normal observed values and cannot be directly used as effective measurement values, it is necessary to reject the gross errors during the data preprocessing. For example, by setting a certain threshold, the data outside the threshold is regarded as a gross error, and these data determined to be gross errors are rejected. Currently, the main methods for rejecting gross errors in monitoring data include the 3σ criterion, statistical test methods, Grubbs' criterion, t-test, etc.

[0060] Exemplarily, the 3σ criterion can be used to reject the gross errors in the monitoring data. The 3σ criterion, also known as the 3σ rule, is a method for judging and rejecting outliers in data. Its core principle is to assume that the data to be processed follows a normal distribution, calculate the mean (μ) and standard deviation (σ) of these data, and set a threshold range to judge whether the data is an outlier. If the error of a certain data point exceeds 3 times the standard deviation, then this data point is considered an outlier and should be rejected.

[0061] It should be noted that in this embodiment, although the 3σ criterion can be well applied to the post-processing of monitoring data, it cannot perform effective real-time monitoring. For example, during the actual monitoring process, if the deformation occurs at the monitoring point of the climbing scaffold, the deformation data may be misidentified as gross error data at this time, resulting in incorrect rejection. Therefore, this embodiment can be improved to perform delay processing on the monitoring data.

[0062] Specifically, since the improved 3σ criterion processes a segment of data, when the data volume is a multiple of the preset processing window, the confidence interval corresponding to the data of the current processing window is given (determined according to the mean and standard deviation), and then it is judged whether the deformation occurs at the monitoring point of the climbing scaffold according to the data within the current processing window, and the gross error rejection is selectively performed according to whether the deformation occurs. For example, when no deformation occurs, the data outside the confidence interval is directly regarded as a gross error and rejected.

[0063] S3. Perform data interpolation on the satellite positioning monitoring data after rejecting the gross errors to obtain the preprocessed data.

[0064] It is understandable that GNSS data may be missing during the processes of acquisition, transmission, and preprocessing (gross error rejection). For these missing data, data interpolation is required. Specifically, the main methods of data interpolation include linear interpolation method, Lagrange interpolation method, cubic spline interpolation method, piecewise cubic Hermite interpolation method, etc. Exemplarily, the embodiment of the present application may adopt the piecewise cubic Hermite interpolation method for data interpolation. This method can not only avoid the non-convergence phenomenon of high-order interpolation functions, but also does not require a complex calculation process, effectively improving the efficiency of data preprocessing.

[0065] S4. Based on the pre-constructed discrete model of the observation system corresponding to the target climbing frame, combined with the preset robust Kalman filter model, filter the preprocessed data to obtain the filtered data.

[0066] It should be noted that the external climbing frame monitoring belongs to short-baseline monitoring and positioning, and is stationary most of the time. Usually, it is only in the moving state when the climbing frame is climbing. Therefore, when establishing a state space model to describe the motion state of the external climbing frame, exemplarily, the instantaneous acceleration of the climbing frame can be regarded as the random interference of the model, that is, regarded as the system noise w(t), and the state vector of the observation point is defined as:

[0067]

[0068] Among them, x(t) represents the position of the observation point, represents the velocity of the observation point.

[0069] It can be represented by the following continuity equation:

[0070]

[0071] Among them, represents the derivative of the state vector X(t), and w(t) represents the system noise, that is, the random interference of the instantaneous acceleration.

[0072] Solving according to the above formula, we get:

[0073]

[0074] Among them, t0 is the initial time, t represents the current time, X(t0) represents the initial state vector, and τ represents the integral variable tk of time t.

[0075] Let t k and t k+1 be the kth and (k + 1)th observation times respectively, and the time interval is Δt = t k+1 -t k . Then, discretize the above formula to obtain the discretized state equation as:

[0076]

[0077] Use the subscripts k + 1 and k to represent t k+1 and t k , solve the above equation, and set:

[0078]

[0079] Substitute into the above discretized state equation to obtain:

[0080]

[0081] Write the above equation in discrete-time form to obtain the state equation of the observation system as:

[0082]

[0083] where, represents X k represents the state vector at the k-th moment, X k+1 represents the state vector at the (k + 1)-th moment, w k represents the system noise within the k-th time interval (between the k-th moment and the (k + 1)-th moment); Δt is the observation time interval. Since the time intervals between two adjacent observations are the same, it can be taken as 1. Thus, the observation equation used to describe the relationship between the observed value and the state vector can be expressed as:

[0084]

[0085] where, y k represents the observed value at the k-th moment, v k represents the observation noise.

[0086] Furthermore, the discretized model of the above observation system (the discretized model of the observation system corresponding to the target climbing frame) can be expressed as:

[0087] x k+1 = Ax k + Γw k ,

[0088] y k = Bx k + v k ,

[0089] where, x k is the system state vector; y k is the observation vector; w k is the system noise; v k is the observation noise; A is the state transition matrix; B is the observation matrix; Γ is the coefficient matrix of the system noise.

[0090] It should be noted that in the classical Kalman filter, the observation noise is usually considered as white noise with a uniform power spectral density distribution of system error and random Gaussian - Markov process, and these errors show randomness and independence. However, in the actual observation process of the building climbing formwork, the observed noise is not randomly distributed, and there are continuous and large gross errors in the data. In these cases, the classical Kalman filter cannot well eliminate and adjust the continuous data fluctuation problem caused by the continuous gross errors, resulting in a large deviation in the final filtering result as time goes on.

[0091] In the embodiment of the present application, for the problem of relatively continuous large deviations in the data, the robust Kalman filter can be further used. Based on the classical Kalman filter, the robust Kalman filter weakens the influence of gross errors on filtering by adjusting the covariance matrix of the observation noise and using the observation covariance matrix to replace the original observation noise covariance matrix. The Kalman filter mainly includes two links: prediction and update. The specific process is as follows:

[0092] Prediction process:

[0093]

[0094] Among them, represents the state prediction at time k + 1, based on the information at time k;

[0095] A d is the state transition matrix, which describes how the state transfers from one time to the next;

[0096] is the state estimate at time k, based on the information at time k;

[0097] B d is the input matrix, which describes how the control input u k affects the system state;

[0098] P k+1∣k is the estimated error covariance at time k + 1, based on the information at time k;

[0099] P k∣k is the estimated error covariance at time k;

[0100] Q d is the process noise covariance matrix, which describes the influence of the random perturbation not captured in the system model on the state.

[0101] Update process:

[0102] 1. Calculation of Kalman gain:

[0103] Kk+1 = P k+1|k H T [HP k+1|k H T + R d -1

[0104] where K k+1 represents the Kalman gain, which is used to determine the influence degree of the observation value on the state estimation;

[0105] P k+1∣k represents the prior estimation error covariance matrix at time k + 1, based on the information at time k;

[0106] represents the observation matrix, which maps the state space to the observation space;

[0107] represents the observation noise covariance matrix, indicating the uncertainty of the observation value.

[0108] 2. State update:

[0109]

[0110] where represents the posterior state estimation at time k + 1, based on the observation value at time k + 1;

[0111] represents the prior state estimation at time k + 1, based on the information at time k;

[0112] z k+1 represents the observation value at time k + 1;

[0113] represents the predicted observation value, based on the prior state estimation.

[0114] 3. Covariance update:

[0115] P k+1|k+1 = [I - K k+1 H]P k+1|k

[0116] where P k+1∣k+1 represents the posterior estimation error covariance matrix at time k + 1, based on the observation value at time k + 1;

[0117] I represents the identity matrix;

[0118] K k+1 H represents the product of the Kalman gain and the observation matrix;

[0119] P k+1∣k represents the prior estimation error covariance matrix at time k + 1, based on the information at time k.​

[0120] It is understandable that, based on the discretization model of the observation system, the robust Kalman filtering model realizes the real-time estimation of the state vector through two links of prediction and update; in the prediction link, the state transition matrix A d and the control input u k are used for state prediction, and in the update link, the observed value and the observation matrix H are used for state update, and the influence of gross error is weakened by adjusting the observation noise covariance matrix.

[0121] S5. Based on the filtered data, the current motion state of the target climbing frame is identified in real time, and based on the monitoring rules corresponding to the current motion state, the target climbing frame is monitored for abnormal states based on the filtered data.

[0122] It should be noted that after filtering the preprocessed data, on the one hand, the filtered data is used to identify the motion state of the climbing frame in real time, and on the other hand, based on the real-time identified motion state of the climbing frame, the corresponding monitoring rules are used to monitor the filtered data for abnormal states.

[0123] Specifically, the current motion state of the target climbing frame may include two types: a stationary state and a climbing state, and each state corresponds to a preset monitoring rule (monitoring threshold). When monitoring the filtered data, the monitoring rule corresponding to the current motion state is used as the judgment basis. Exemplarily, if the monitored value exceeds the current preset threshold, it is determined that the target climbing frame is abnormal (there is an abnormal state).

[0124] Please refer to Figure 2 , in the embodiments of the present application, by obtaining the monitoring data of the climbing frame based on satellite positioning technology, and preprocessing the monitoring data through gross error rejection, data interpolation, robust Kalman filtering, etc., finally, by identifying the motion state of the climbing frame and based on different rules to monitor the state of the climbing frame dynamically, the comprehensiveness and reliability of the climbing frame state monitoring are effectively improved.

[0125] In some possible embodiments, step S2 may include:

[0126] S201. Taking a preset data volume as a processing window, based on the satellite positioning monitoring data, the confidence interval corresponding to the current processing window is determined by using the 3-sigma rule;

[0127] S202. Based on the confidence interval, it is judged whether the target climbing frame is deformed within the current processing window;

[0128] S203. In the case that the target climbing frame is not deformed, the gross error rejection of the satellite positioning monitoring data of the current processing window is performed based on the confidence interval;

[0129] S204. In the case where the target climbing frame is deformed, adjust the confidence interval, and based on the adjusted confidence interval, reject the gross errors in the satellite positioning monitoring data of the current processing window.

[0130] It should be noted that in the case where the target climbing frame is not deformed, the data regarded as gross errors in the satellite positioning monitoring data can be directly rejected based on the confidence interval calculated for the current processing window; if the target climbing frame is deformed, then the confidence interval calculated for the current processing window needs to be adjusted, for example, the confidence interval is increased to a certain extent, and then based on the adjusted confidence interval, the gross errors in the satellite positioning monitoring data of the current processing window are rejected.

[0131] Based on this, by identifying the deformation situation of the climbing frame and using different confidence intervals to reject gross errors respectively according to the deformation situation, the influence of the deformation situation on the process of rejecting gross errors can be excluded, and the reliability of the preprocessing of the monitoring data can be further improved.

[0132] In some possible embodiments, step S202 may include:

[0133] S2021. Identify the gross error data within the current processing window from the satellite positioning monitoring data based on the confidence interval, and count the corresponding amount of gross error data;

[0134] S2022. Determine whether the target climbing frame is deformed within the current processing window based on whether the amount of gross error data exceeds a preset data volume threshold.

[0135] It should be noted that in the process of identifying whether the target climbing frame is deformed within the current processing window, the gross error data exceeding the confidence interval within the current processing window can be first counted. When the amount of these gross error data exceeds the preset data volume threshold, it indicates that the mean value and standard deviation (confidence interval) of the data corresponding to the current processing window have changed significantly, and it is regarded that the target climbing frame has deformed during the time of the current processing window. At this time, the originally calculated confidence interval will no longer be applicable to the rejection of gross errors.

[0136] In some possible embodiments, step S4 may include:

[0137] S401. Obtain the observation system discrete model corresponding to the target climbing frame constructed in advance, construct an observation covariance matrix based on the IGG3 equivalent weight function, and use the observation covariance matrix to replace the observation noise covariance matrix of the traditional Kalman filter model to obtain a robust Kalman filter model;

[0138] S402. Combine the observation system discrete model and the preset robust Kalman filter model to perform filtering processing on the preprocessed data to obtain the filtered data.

[0139] It should be noted that for robust Kalman filtering, the key to its filtering process of data lies in the selection method of the equivalent weight. Different equivalent weight functions will result in different equivalent observation covariance matrices, thereby producing different filtering results. Commonly used equivalent weight construction functions include Huber, IGG1, and IGG3 equivalent weight functions.

[0140] Exemplarily, the embodiment of the present application adopts the IGG3 equivalent weight function for robust estimation. The IGG3 weight function is proposed and improved based on the boundedness of measurement errors. On the basis of the two-segment division of IGG1, additional division segments are added, including the normal domain, the suspicious domain, and the elimination domain. Among them, for the observed values in the normal domain, no processing is performed and the original weight is adopted. For the suspicious domain, the observed values are down-weighted for processing. For the elimination domain with too large residual differences, the observed values are directly eliminated and their weights are set to 0. The IGG3 equivalent weight function is very effective for the case of many gross errors in observed values and can be expressed as:

[0141]

[0142] where P i is the weight matrix corresponding to the observed value; v is the standardized residual in the Kalman filtering process; the value of k0 is generally 1.0 - 1.5; the value of k1 is generally 3.0 - 4.5. Iterative calculation is performed after each filtering update until the difference between the state filtering value at the t-th iteration and the state filtering value at the (t - 1)-th iteration is less than the set limit error, then the iteration ends.

[0143] In some possible embodiments, step S4 may include:

[0144] S411. During the prediction and update process of the robust Kalman filtering model, height constraints are imposed on the predicted value of each iteration based on a preset height constraint condition;

[0145] Among them, within the preset initial monitoring period, a preset height threshold is used as the height constraint condition; within the remaining monitoring periods except the initial monitoring period, real-time calculation is performed based on a preset angle threshold to determine the corresponding height constraint condition.

[0146] It should be noted that the climbing frame monitoring belongs to the scenario of short baseline monitoring and positioning. Short multipath effects usually refer to those multipath effects that occur within a very short time (such as a few milliseconds to a few seconds), and are usually more obvious in urban canyons or environments with high-rise buildings in close proximity; since the climbing frame monitoring stations are usually set up in the middle of the city, short multipath effects will cause rapid changes in the signal strength and phase measured by the receiver, thereby affecting the positioning accuracy.

[0147] It is understandable that adding constraints in the height direction during positioning can effectively suppress the influence of short multipath. Therefore, height constraint estimation is introduced in each iteration of the robust Kalman filtering process in this embodiment.

[0148] Due to the design problem of the high-rise building, the external climbing frame does not produce deviation during the initial rising stage but remains in vertical ascent. In the later stage of construction, due to the torsional design of the high-rise building, there will be an angle θ between the climbing frame rising route and the horizontal direction at this time. Therefore, the following inequality constraints can be introduced in each iteration of the robust Kalman filtering to constrain the height estimation:

[0149]

[0150] where U k represents the displacement observation value at time k in the U direction (vertical direction), N k represents the displacement observation value at time k in the N direction (north direction), and E k represents the displacement observation value at time k in the E direction (east direction).

[0151] Since the climbing frame basically rises vertically in the early stage of monitoring, the constraint height threshold can be directly set. In the later stage of monitoring, the threshold of the height constraint needs to be determined by the angle between the climbing frame rising route and the horizontal direction. Therefore, the overall filtering height constraint of the climbing frame is as follows:

[0152]

[0153] where h th represents the set height threshold, and θ th represents the set angle threshold.

[0154] In some possible embodiments, in step S5, based on the filtered data, real-time identification of the current motion state of the target climbing frame may include:

[0155] S501. Based on the filtered data, use a preset sliding reference window and a sliding detection window to calculate the mean value of the corresponding sequence points in each window in real time; wherein, the sliding reference window and the sliding detection window are configured with a preset overlapping length;

[0156] S502. Calculate the difference between the mean values of the sequence points of the sliding reference window and the sliding detection window in real time, and determine the current motion state of the target climbing frame based on the magnitude relationship between the difference within a preset time period and a preset threshold.

[0157] It should be noted that the CUSUM algorithm based on a sliding window is an effective event detection method. Therefore, during the monitoring process of the climbing formwork state, the principle of CUSUM can be utilized to set appropriate parameters, which can automatically detect state changes in the input time series signal. This method has a simple logic and strong anti-interference ability.

[0158] As Figure 3 shown, in the coordinate time series in the U direction based on satellite positioning monitoring data, a reference window (sliding reference window) w m and an upward detection window (sliding detection window) w n are defined. The upward detection window is adjacent to the reference window at the back. At the same time, to ensure the coupling degree between height changes in the window, there is a certain overlapping part between the two windows.

[0159] The lengths of the reference window and the upward detection window are set to m and n respectively, and the overlapping length is set to x. Considering that currently the climbing formwork only has vertical climbing, and the height of each climb is about 4.5m, and the sampling interval is 1s, the lengths of the reference window and the upward detection window can be set to 60, and the overlapping length can be set to 30. Then, the means of the sequence points in the two windows are calculated respectively, and the calculation formula is as follows:

[0160]

[0161] Among them, M m represents the mean of the sequence points in the reference window, M n represents the mean of the sequence points in the reference window, k represents the sampling point where the calculation starts, which is located at the end of the upward detection window; p(j) is the coordinate value of the jth sequence point.

[0162] During climbing, in the early stage, only vertical climbing is carried out, and the speed and distance of each rise are relatively consistent. Therefore, only the comparison between the upward change amount and the threshold needs to be considered. The difference between the means of the sequence points in the two windows is used as the characteristic quantity for judging the upward event, and the calculation formula is as follows:

[0163] D k = M m - M n

[0164] When the characteristic quantity is positive and most of it exceeds the set threshold for a period of time, it is considered that the upward behavior starts at the first endpoint of the detection window that first exceeds the threshold. When the characteristic quantity changes from continuously exceeding the threshold to gradually approaching and finally continuously being less than the threshold and remaining stable, it is considered that the end position of the detection window where the threshold is exceeded for the last time is the end of the upward movement.

[0165] Based on this, a sliding detection window and a reference window can be used to obtain characteristic quantities for judging the movement state of the climbing frame, and based on the comparison between the characteristic quantities and the threshold, it can be determined whether the target climbing frame is in a climbing state or a stationary state.

[0166] In some possible embodiments, in step S5, based on the monitoring rules corresponding to the current movement state, abnormal state monitoring of the target climbing frame based on the filtered data may include:

[0167] S511. Based on the filtered data, use the CUSUM algorithm to statistically calculate the offset statistic of the sequence data relative to a preset reference value in real time;

[0168] S512. Determine whether the target climbing frame has an abnormal state according to whether the offset statistic exceeds a preset warning threshold; wherein, the warning threshold is determined based on the monitoring rules corresponding to the current movement state.

[0169] It should be noted that the CUSUM algorithm is a statistical tool for quality control, widely used in industrial and engineering fields to detect small and continuous changes in processes. Its core lies in the two concepts of cumulative deviation and accumulation. Deviation refers to the difference between the observed value and the target value, and accumulation is to accumulate these deviations over time. The CUSUM algorithm is very sensitive to small mean changes, applicable to univariate and multivariate data, and can perform retrospective analysis.

[0170] Based on this, the embodiments of the present application can use the CUSUM algorithm to statistically calculate the characteristic quantities for abnormal state monitoring of the climbing frame. Specifically, first define the CUSUM statistic when there is an upward offset:

[0171]

[0172] wherein, represents the positive abnormal change amount (cumulative sum) in the monitoring data sequence, and the initial value is usually set to 0, that is x n represents the observed value at the nth time step in the filtered data; k represents a preset threshold for controlling the sensitivity of the cumulative sum; μ0 represents the mean value of the sequence data calculated over a period of time.

[0173] Similarly, the cumulative sum of the downward offset can be expressed as:

[0174]

[0175] Based on this, by calculating and When or When the target climbing frame is judged to be in an abnormal state, an early warning is triggered; where h1 and h2 are limit values ​​set according to demand.

[0176] It should be noted that, in practical applications, the accumulated sums can be compared by setting a plurality of different limit values ​​to achieve graded early warning of abnormal conditions of the climbing frame.

[0177] Please refer to Figure 4 , Figure 4 The following is a block diagram showing the components of the state monitoring device for the building climbing frame provided in some embodiments of the present application. It should be understood that the state monitoring device for the building climbing frame is similar to the above-mentioned Figure 1 Corresponding to the method embodiment, it is able to execute each step involved in the above method embodiment. The specific functions of the status monitoring device of the building climbing frame can be found in the description above. To avoid repetition, the detailed description is appropriately omitted here.

[0178] Figure 4 The state monitoring device of the building climbing frame includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the state monitoring device of the building climbing frame, and the state monitoring device of the building climbing frame includes:

[0179] The data acquisition module 410 is used to acquire the satellite positioning monitoring data of the target climbing frame;

[0180] The gross error elimination module 420 is used to use a preset data volume as a processing window, dynamically identify the deformation of the target climbing frame within the current processing window based on the satellite positioning monitoring data, and eliminate gross errors from the satellite positioning monitoring data of the current processing window in combination with the deformation;

[0181] The data interpolation module 430 is used to interpolate the satellite positioning monitoring data after the gross error elimination to obtain pre-processed data;

[0182] The data filtering module 440 is used to filter the pre-processed data based on the observation system discrete model corresponding to the target climbing frame constructed in advance and in combination with the preset robust Kalman filter model to obtain filtered data;

[0183] The state monitoring module 450 is used to identify the current motion state of the target climbing frame in real time based on the filtered data, and perform abnormal state monitoring of the target climbing frame based on the filtered data according to the monitoring rules corresponding to the current motion state.

[0184] It can be understood that the above-mentioned device item embodiment corresponds to the method item embodiment of the present invention. The state monitoring device of a building climbing frame provided by the embodiment of the present invention can implement the state monitoring method of a building climbing frame provided by any method item embodiment of the present invention.

[0185] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process of the above-described device can refer to the corresponding process in the foregoing method, and will not be elaborated herein.

[0186] As Figure 5 shown, some embodiments of the present application provide an electronic device 500, which includes: a memory 510, a processor 520, and a computer program stored on the memory 510 and executable on the processor 520. Among them, when the processor 520 reads the program from the memory 510 through the bus 530 and executes the program, it can implement the method of any embodiment included in the above-described state monitoring method of the building climbing frame.

[0187] The processor 520 can process digital signals and can include various computing structures. For example, a complex instruction set computer structure, a reduced instruction set computer structure, or a structure that implements a combination of multiple instruction sets. In some examples, the processor 520 can be a microprocessor.

[0188] The memory 510 can be used to store instructions executed by the processor 520 or data related to the execution of the instructions. These instructions and / or data can include code for implementing some or all of the functions of one or more modules described in the embodiments of the present application. The processor 520 of the present disclosure embodiment can be used to execute the instructions in the memory 510 to implement the method shown above. The memory 510 includes a dynamic random access memory, a static random access memory, a flash memory, an optical memory, or other memories well known to those skilled in the art.

[0189] Some embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, it executes the method described in the method embodiment.

[0190] Some embodiments of the present application also provide a computer program product, which, when running on a computer, causes the computer to execute the method described in the method embodiment.

[0191] It should be noted that the embodiments in this specification are all described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0192] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0193] In addition, each functional module in various embodiments of the present application can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0194] If the described functions are implemented in the form of software functional modules and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0195] The above are only embodiments of the present application and are not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application. It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0196] As described above, these are only the specific implementation manners of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily conceive of changes or replacements within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claimed rights.

[0197] It should be noted that in this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

Claims

1. A method for monitoring the state of a building climbing scaffold, characterized in that, Including: Obtaining satellite positioning monitoring data of the target climbing formwork; Taking a preset data volume as a processing window, dynamically identifying the deformation condition of the target climbing formwork within the current processing window based on the satellite positioning monitoring data, and combining the deformation condition to eliminate gross errors from the satellite positioning monitoring data of the current processing window; Performing data interpolation on the satellite positioning monitoring data after gross error elimination to obtain preprocessed data; Based on the pre-established discrete observation system model corresponding to the target climbing formwork, combining a preset robust Kalman filter model to perform filtering processing on the preprocessed data to obtain filtered data; Based on the filtered data, real-time identifying the current motion state of the target climbing formwork, and according to the monitoring rules corresponding to the current motion state, monitoring the abnormal state of the target climbing formwork based on the filtered data; The real-time identifying the current motion state of the target climbing formwork based on the filtered data includes: Based on the filtered data, using a preset sliding reference window and a sliding detection window to calculate the mean value of the corresponding sequence points within each window in real time; wherein, the sliding reference window and the sliding detection window are configured with a preset overlapping length; Real-time calculating the difference between the mean values of the sequence points of the sliding reference window and the sliding detection window, and determining the current motion state of the target climbing formwork based on the magnitude relationship between the difference within a preset time period and a preset threshold value.

2. The method for monitoring the state of the building climbing formwork according to claim 1, wherein The taking a preset data volume as a processing window, dynamically identifying the deformation condition of the target climbing formwork within the current processing window based on the satellite positioning monitoring data, and combining the deformation condition to eliminate gross errors from the satellite positioning monitoring data of the current processing window includes: Taking a preset data volume as a processing window, and based on the satellite positioning monitoring data, using the 3-sigma rule to determine the confidence interval corresponding to the current processing window; Judging whether the target climbing formwork deforms within the current processing window based on the confidence interval; In the case that the target climbing formwork does not deform, eliminating gross errors from the satellite positioning monitoring data of the current processing window based on the confidence interval; In the case that the target climbing formwork deforms, adjusting the confidence interval, and eliminating gross errors from the satellite positioning monitoring data of the current processing window based on the adjusted confidence interval.

3. The state monitoring method of the construction climbing formwork according to claim 2, characterized in that The judging whether the target climbing formwork deforms within the current processing window based on the confidence interval includes: Identifying gross error data within the current processing window from the satellite positioning monitoring data based on the confidence interval, and counting the corresponding amount of gross error data; Determining whether the target climbing formwork deforms within the current processing window based on whether the amount of gross error data exceeds a preset data volume threshold.

4. The state monitoring method of the building climbing frame according to claim 1, wherein The based on the pre-established discrete observation system model corresponding to the target climbing formwork, combining a preset robust Kalman filter model to perform filtering processing on the preprocessed data to obtain filtered data includes: Obtain the discrete model of the observation system corresponding to the pre-built target climbing formwork, construct the observation covariance matrix based on the IGG3 equivalent weight function, and replace the observation noise covariance matrix of the traditional Kalman filter model with the obtained observation covariance matrix to obtain a robust Kalman filter model; Filter the pre-processed data by combining the discrete model of the observation system and the preset robust Kalman filter model to obtain the filtered data.

5. The method for monitoring the state of the building climbing formwork according to claim 1, characterized in that, The filtering the pre-processed data by combining the discrete model of the observation system corresponding to the pre-built target climbing formwork and the preset robust Kalman filter model to obtain the filtered data includes: During the prediction and update processes of the robust Kalman filter model, perform height constraint on the predicted value of each iteration based on the preset height constraint condition; Among them, during the preset initial monitoring period, use the preset height threshold as the height constraint condition; during the remaining monitoring periods except the initial monitoring period, perform real-time calculation based on the preset angle threshold and determine the corresponding height constraint condition.

6. The status monitoring method of the building climbing formwork according to claim 1, characterized in that The abnormal state monitoring of the target climbing formwork based on the filtered data according to the monitoring rule corresponding to the current motion state includes: Based on the filtered data, use the CUSUM algorithm to statistically calculate the deviation statistic of the sequence data relative to the preset reference value in real time; Determine whether the target climbing formwork is in an abnormal state according to whether the deviation statistic exceeds the preset warning threshold; wherein, the warning threshold is determined based on the monitoring rule corresponding to the current motion state.

7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. Among them, when the processor executes the program, it can implement the state monitoring method of the construction climbing formwork according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is run by the processor, it executes the state monitoring method of the construction climbing formwork according to any one of claims 1-6.

9. A computer program product, characterized in that, The computer program product includes a computer program, and when the computer program is executed by the processor, it implements the state monitoring method of the construction climbing formwork according to any one of claims 1-6.

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