State monitoring method and device for building climbing frame, medium and program product

Abstract

The embodiment of the invention provides a building climbing frame state monitoring method and device, a medium and a program product. The method comprises the following steps: acquiring satellite positioning monitoring data of a climbing frame; identifying the deformation condition of the target climbing frame, and performing gross error elimination on the satellite positioning monitoring data in combination with the deformation condition; performing data interpolation on the data after gross error elimination; based on the observation system discrete model, filtering the preprocessed data in combination with a robust Kalman filtering model; and recognizing the current motion state of the target climbing frame in real time based on the filtered data, and monitoring the abnormal state of the target climbing frame based on the filtered data according to the current motion state. According to the embodiment of the invention, the monitoring data of the climbing frame is obtained based on the satellite positioning technology, and the monitoring data is preprocessed through gross error elimination, data interpolation, robust Kalman filtering and the like, so that the state of the climbing frame is dynamically monitored, and the comprehensiveness and reliability of the state monitoring of the climbing frame are effectively improved.

Description

Technical Field

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

[0002] With the acceleration of urbanization and the continuous development of modern construction technology, the number of super-high-rise buildings continues to rise. In the process of high-altitude construction, the safety of the external climbing frame, as a key supporting equipment, directly affects the smooth progress of construction and the quality of the project. In the complex environment of the city center, the stability and operation status of the external climbing frame are easily affected by various external factors such as wind load, vibration, temperature change, etc. 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 the safety of construction, evaluating the health status of the structure, and implementing safety warnings.

[0003] At present, with regard to the monitoring of external climbing frames in construction, on the one hand, existing research mainly focuses on the monitoring of the structural status of the climbing frames themselves, and on the other hand, the impact of external loads on the climbing frames has also attracted much attention. Although existing monitoring solutions have improved the efficiency and automation level of climbing frame safety monitoring to a certain extent, these solutions often only focus on one-sided perspectives and cannot fully, accurately and reliably realize dynamic monitoring of the climbing frame status. 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 status of a building climbing frame, so as to improve the comprehensiveness and reliability of the monitoring of the status of the climbing frame.

[0005] In a first aspect, an embodiment of the present application provides a method for monitoring the state of a building climbing frame, comprising:

[0006] Obtain satellite positioning monitoring data of the target climbing frame;

[0007] Taking the preset data volume as the processing window, dynamically identifying the deformation of the target climbing frame within the current processing window based on the satellite positioning monitoring data, and removing gross errors from the satellite positioning monitoring data of the current processing window in combination with the deformation;

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

[0009] Based on the pre-constructed observation system discrete model corresponding to the target climbing frame, the pre-processed data is filtered in combination with a preset robust Kalman filter model to obtain filtered data;

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

[0011] In an embodiment of the present application, monitoring data of the climbing frame is acquired based on satellite positioning technology, and the monitoring data is preprocessed through gross error elimination, data interpolation, and robust Kalman filtering. Finally, the movement state of the climbing frame is identified and the dynamic state of the climbing frame is monitored based on different rules, thereby effectively improving the comprehensiveness and reliability of the climbing frame state monitoring.

[0012] In some possible embodiments, the preset data volume is used as a processing window, the deformation of the target climbing frame in the current processing window is dynamically identified based on the satellite positioning monitoring data, and the satellite positioning monitoring data of the current processing window is subjected to gross error elimination in combination with the deformation, including:

[0013] Taking a preset amount of data as a processing window, based on the satellite positioning monitoring data, and using the Laida criterion to determine a confidence interval corresponding to the current processing window;

[0014] Determining whether the target climbing frame is deformed within the current processing window based on the confidence interval;

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

[0016] In the case where the target climbing frame is deformed, the confidence interval is adjusted, and gross errors of the satellite positioning monitoring data in the current processing window are eliminated based on the adjusted confidence interval.

[0017] In the embodiment of the present application, the reliability of monitoring data preprocessing is further improved by identifying the deformation of the climbing frame and eliminating the influence of the deformation on the gross error elimination process.

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

[0019] Based on the confidence interval, identifying gross error data within a current processing window from the satellite positioning monitoring data, and counting the corresponding gross error data amount;

[0020] Whether the target climbing frame is deformed within the current processing window is determined based on whether the amount of gross error data exceeds a preset data amount threshold.

[0021] In an embodiment of the present application, the amount of gross error data in each processing window is counted as a basis for determining whether the climbing frame has been deformed, thereby further improving the reliability of monitoring data preprocessing.

[0022] In some possible embodiments, the pre-constructed observation system discrete model corresponding to the target climbing frame is combined with a preset robust Kalman filter model to filter the pre-processed data to obtain the filtered data, including:

[0023] Obtain a pre-constructed observation system discrete 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] The preprocessed data is filtered in combination with the observation system discrete model and a preset robust Kalman filter model to obtain filtered data.

[0025] In an embodiment of the present application, by adopting the IGG3 equivalent weight function to perform robust estimation, the filtering process can better handle the noise and errors in the observed data, thereby effectively improving the accuracy of data filtering.

[0026] In some possible embodiments, the pre-constructed observation system discrete model corresponding to the target climbing frame is combined with a preset robust Kalman filter model to filter the pre-processed data to obtain the filtered data, including:

[0027] In the prediction and updating process of the robust Kalman filter model, a height constraint is imposed on the predicted value of each iteration based on a preset height constraint condition;

[0028] Among them, in a preset initial monitoring period, a preset height threshold is used as the height constraint condition; in the remaining monitoring periods except the initial monitoring period, real-time calculation is performed based on a preset angle threshold and the corresponding height constraint condition is determined.

[0029] In the embodiment of the present application, the short multipath effect of the data monitoring process is weakened by utilizing the height direction constraint condition, thereby further optimizing the accuracy of dynamic monitoring of positioning data.

[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 means of the corresponding sequence points in each window are calculated in real time 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 overlap length;

[0032] The difference between the sequence point means 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 relationship between the difference within a preset time period and a preset threshold.

[0033] In the embodiment of the present application, feature quantities for determining the motion state of the climbing frame are obtained based on a sliding detection window and a reference window, thereby further improving the accuracy of motion state identification of the climbing frame.

[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 CUSUM algorithm is used to calculate the deviation statistics of the sequence data relative to the preset reference value in real time;

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

[0037] In an embodiment of the present application, the offset cumulative sum of the statistical sequence data based on the CUSUM algorithm is used as a basis for judging whether the climbing frame is in an abnormal state, thereby further improving the accuracy of the climbing frame status monitoring.

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

[0039] A data acquisition module is used to obtain satellite positioning monitoring data of the target climbing frame;

[0040] A gross error elimination module 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;

[0041] A data interpolation module is used to interpolate the satellite positioning monitoring data after gross error elimination to obtain pre-processed data;

[0042] A data filtering module is used to filter the preprocessed data based on a pre-constructed observation system discrete model corresponding to the target climbing frame in combination with a preset robust Kalman filter model to obtain filtered data;

[0043] The state monitoring module 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 on the target climbing frame 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, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement the method described in any embodiment of the first aspect when executing the program.

[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 executed 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, wherein the computer program product includes a computer program, wherein 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 for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A schematic diagram of a flow chart of a state monitoring method of a building climbing frame provided in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of the overall process of the state monitoring method of the building climbing frame provided in the embodiment of the present application;

[0050] Figure 3 A schematic diagram of a dual sliding window event detection model provided in an embodiment of the present application;

[0051] Figure 4 A schematic diagram of the structure of a state monitoring device for a building climbing frame provided in an embodiment of the present application;

[0052] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0054] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so 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 this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

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

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

[0057] First, GNSS (Global Navigation Satellite System) integrated receivers are deployed at key locations on the building climbing frame (such as the four corners, etc.), and a base station is set up outside the building. Four-system satellite signals are obtained through dual-frequency observation, including GPS (Global Positioning System) data, Galileo (Galileo Satellite Navigation System) data, and BDS (Beidou Satellite Navigation System) data. These raw 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, data of a key point is generated as the satellite positioning monitoring data of the target climbing frame.

[0058] S2. Using the preset data volume as the 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 in the satellite positioning monitoring data of the current processing window based on the deformation.

[0059] It should be noted that in the monitoring of super-high-rise external climbing frames, since the scene is in a typical urban canyon area, the occlusion and reflection of the buildings cause non-line-of-sight (NLOS) errors and multipath effects in the GNSS signal. At the same time, the number of visible satellites is reduced, and the geometric layout of the visible satellites is poor relative to the open environment. This leads to problems such as the ambiguity being unfixed at certain moments during the real-time solution, and the solution results have a serious drift phenomenon. Since these original monitoring data may be very different from the normal observation values ​​and cannot be used directly as valid measurement values, it is necessary to eliminate gross errors in the process of data preprocessing. For example, by setting a certain threshold, the data outside the threshold is regarded as gross errors, and the data judged as gross errors are eliminated. At present, the main methods for eliminating gross errors in monitoring data include the Raida criterion, statistical test method, Grubbs criterion, t-test, etc.

[0060] For example, the Laida criterion can be used to eliminate gross errors in monitoring data. The Laida criterion, also known as the 3σ criterion, is a method for judging and eliminating outliers in data. Its core principle is to assume that the data to be processed follows a normal distribution, and by calculating the mean (μ) and standard deviation (σ) of these data, a threshold range is set to determine whether the data is an outlier. If the error of a data point exceeds 3 times the standard deviation, the data point is considered to be an outlier and should be eliminated.

[0061] It should be noted that, in this embodiment, although the Laida criterion can be well applied to the post-processing of monitoring data, it cannot perform effective real-time monitoring. For example, in the actual monitoring process, if the climbing frame monitoring point is deformed, the deformation data may be mistaken for gross error data and thus be erroneously eliminated. Therefore, this embodiment can be improved to perform delayed processing of the monitoring data.

[0062] Specifically, since the improved Laida criterion processes a section of data, when the data amount is a multiple of the preset processing window, a confidence interval corresponding to the data in the current processing window is given (determined according to the mean and standard deviation), and then whether the climbing frame monitoring point is deformed is judged based on the data in the current processing window, and then gross errors are selectively eliminated based on whether deformation occurs. For example, if no deformation occurs, the data beyond the confidence interval will be directly regarded as a gross error and eliminated.

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

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

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

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

[0067]

[0068] Among them, x(t) represents the location of the observation point, Represents the speed of the observation point.

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

[0070]

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

[0072] According to the above formula, we can 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 at time t.

[0075] Assume t k and t k+1 are the kth and k+1th observation moments, respectively, with a time interval of Δt=t k+1 -t k , then discretize the above formula and get the discretized state equation as follows:

[0076]

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

[0078]

[0079] Substituting into the above discretized state equation, we get:

[0080]

[0081] Writing the above equation in discrete time form, the state equation of the observation system is obtained as:

[0082]

[0083] Among them, represents X k represents the state vector at the kth moment, X k+1 represents the state vector at the k+1th moment, w k represents the system noise in the kth time interval (between the kth moment and the k+1th 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 observation value and the state vector can be expressed as:

[0084]

[0085] Among them, y k represents the observed value at the kth moment, v k represents the observation noise.

[0086] Furthermore, the discretization model of the above observation system (the discrete 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] Among them, 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 transfer matrix; B is the observation matrix; Γ is the coefficient matrix of system noise.

[0090] It should be noted that in the classical Kalman filter, the observation noise is usually considered to be a systematic error, white noise with uniform power spectrum density distribution, and a random Gauss-Markov process, and these errors show randomness and independence. However, in the actual observation process of building climbing frames, 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 effectively eliminate and adjust the continuous data fluctuation problem caused by continuous gross errors, resulting in a large deviation in the final filtering result over time.

[0091] In the embodiment of the present application, in order to solve the problem of relatively continuous large deviations in the data, a robust Kalman filter can be further used. Based on the classic Kalman filter, the robust Kalman filter adjusts the covariance matrix of the observation noise and replaces the original observation noise covariance matrix with the observation covariance matrix to weaken the influence of gross errors on filtering. Kalman filtering mainly includes two links: prediction and update. The specific process is as follows:

[0092] Prediction process:

[0093]

[0094] in, 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 is transferred from one moment 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, describing the control input u k How to affect 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 impact of random disturbances on the state that are not captured in the system model.

[0101] Update process:

[0102] 1. Kalman gain calculation:

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

[0104] Among them, K k+1 represents the Kalman gain, which is used to determine the influence 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, which represents the uncertainty of the observations.

[0108] 2. Status update:

[0109]

[0110] in, represents the posterior state estimate at time k+1, based on the observation at time k+1;

[0111] represents the prior state estimate 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, based on the prior state estimate.

[0114] 3. Covariance update:

[0115] P k+1|k+1 =[IK k+1 H]P k+1|k

[0116] Among them, P k+1∣k+1 represents the posterior estimation error covariance matrix at time k+1, based on the observations at time k+1;

[0117] I represents the identity matrix;

[0118] K k+1 H represents the product of Kalman gain and 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 can be understood that based on the discretization model of the observation system, the robust Kalman filter model realizes the real-time estimation of the state vector through two links: prediction and update. The prediction link uses the state transfer matrix A d and control input u k To perform state prediction, the update phase uses the observation values ​​and the observation matrix H to update the state, and weakens the impact of gross errors 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 according to the monitoring rules corresponding to the current motion state, the target climbing frame is monitored for abnormal conditions based on the filtered data.

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

[0123] Specifically, the current motion state of the target climbing frame may include a stationary state and a climbing state. Each state corresponds to a preset monitoring rule (monitoring threshold). When monitoring the filtered data, the monitoring rules corresponding to the current motion state are used as the basis for judgment. For example, if the monitoring value exceeds the current preset threshold, it is determined that the target climbing frame is abnormal (an abnormal state exists).

[0124] See also Figure 2 In an embodiment of the present application, monitoring data of the climbing frame is acquired based on satellite positioning technology, and the monitoring data is preprocessed through gross error elimination, data interpolation, and anti-error Kalman filtering. Finally, the movement state of the climbing frame is identified and the dynamic state of the climbing frame is monitored based on different rules, thereby effectively improving the comprehensiveness and reliability of the climbing frame state monitoring.

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

[0126] S201, using a preset data volume as a processing window, based on satellite positioning monitoring data, and using the Laida criterion to determine a confidence interval corresponding to the current processing window;

[0127] S202, judging whether the target climbing frame is deformed within the current processing window based on the confidence interval;

[0128] S203, when the target climbing frame is not deformed, the satellite positioning monitoring data in the current processing window is subjected to gross error elimination based on the confidence interval;

[0129] S204: When the target climbing frame is deformed, the confidence interval is adjusted, and gross errors of the satellite positioning monitoring data in the current processing window are eliminated based on the adjusted confidence interval.

[0130] It should be noted that when the target climbing frame has not been deformed, the data regarded as gross errors in the satellite positioning monitoring data can be directly eliminated based on the confidence interval calculated in the current processing window; if the target climbing frame is deformed, it is necessary to adjust the confidence interval calculated in the current processing window, for example, increase the confidence interval to a certain extent, and then eliminate gross errors in the satellite positioning monitoring data of the current processing window based on the adjusted confidence interval.

[0131] Based on this, by identifying the deformation of the climbing frame and using different confidence intervals to eliminate gross errors according to the deformation conditions, the influence of the deformation conditions on the gross error elimination process can be eliminated, further improving the reliability of monitoring data preprocessing.

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

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

[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 amount threshold.

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

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

[0137] S401, obtaining a pre-constructed observation system discrete model corresponding to the target climbing frame, constructing an observation covariance matrix based on the IGG3 equivalent weight function, and using 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 , filtering the preprocessed data in combination with the observation system discrete model and the preset robust Kalman filter model to obtain filtered data.

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

[0140] Exemplarily, the embodiment of the present application adopts the IGG3 equivalent weight function to perform robust estimation. The IGG3 weight function is an improvement based on the boundedness of the measurement error. It adds division segments based on the two-segment division of IGG1, including the normal domain, the suspicious domain, and the eliminated domain. Among them, no processing is done on the observations in the normal domain, and the original weights are used. For the suspicious domain, the observations are downgraded before processing, and for the eliminated domain with excessive residual differences, the observations are directly eliminated so that their weights are 0. The IGG3 equivalent weight function is very effective for situations where there are many gross errors in the observations, which can be expressed as:

[0141]

[0142] Among them, P i is the weight matrix corresponding to the observed value; v is the standardized residual in the Kalman filter process; the value of k0 is generally 1.0-1.5; the value of k1 is generally 3.0-4.5. After each filter update, iterative calculation is performed until the difference between the state filter value of the tth iteration and the state filter value of the t-1th iteration is less than the set limit, then the iteration ends.

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

[0144] S411, in the prediction and update process of the robust Kalman filter model, a height constraint is imposed on the predicted value of each iteration based on a preset height constraint condition;

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

[0146] It should be noted that climbing frame monitoring belongs to the scenario of short baseline monitoring and positioning. Short multipath effects usually refer to those multipath effects that occur in a very short time (such as a few milliseconds to a few seconds), which are usually more obvious in urban canyons or environments with tall buildings; since climbing frame monitoring sites 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 can be understood that adding constraints on the altitude direction during positioning can effectively suppress the influence of short multipaths, so altitude constraint estimation is introduced in each iteration of the robust Kalman filtering process of this embodiment.

[0148] Due to the design of the high-rise building, the external climbing frame will not deviate when it rises in the early stage, but will remain in a vertical rise. In the later stage of construction, due to the twisting design of the high-rise building, there will be an angle θ between the climbing frame and the horizontal direction. Therefore, the following inequality constraints can be introduced in each iteration of the robust Kalman filter to constrain the height estimation:

[0149]

[0150] Among them, 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), E k Represents the observed displacement 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 height constraint threshold needs to be determined by the angle between the climbing frame's rising route and the horizontal direction. Therefore, the overall filtering height constraint of the climbing frame is as follows:

[0152]

[0153] Among them, h th Represents the set height threshold, θ th Indicates the set angle threshold.

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

[0155] S501, based on the filtered data, using a preset sliding reference window and a sliding detection window to calculate the mean 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 overlap length;

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

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

[0158] like Figure 3 As shown in the figure, based on the coordinate time series of satellite positioning monitoring data in the U direction, a reference window (sliding reference window) w is defined m And the rising detection window (sliding detection window) w n , where the rising detection window is adjacent to the reference window, and in order to ensure the degree of coupling between the height changes in the windows, there is a certain overlap between the two windows.

[0159] The lengths of the reference window and the rise detection window are set to m and n respectively, and the overlap length is set to x. Considering that the climbing frame currently only has vertical climbing, the height of each climb is about 4.5m, and the sampling interval is 1s, the lengths of the reference window and the rise detection window can be set to 60, and the overlap length can be set to 30. Then, the mean of the sequence points in the two windows is 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 rising detection window; p(j) is the coordinate value of the jth sequence point.

[0162] When climbing, only vertical climbing is performed in the early stage, and the speed and distance of each ascent are relatively consistent. Therefore, it is only necessary to compare the ascent change with the threshold. The difference between the mean values ​​of the sequence points of the two windows is used as the feature quantity for judging the ascent event. The calculation formula is as follows:

[0163] D k =M m -M n

[0164] When the feature quantity is positive and most of it exceeds the set threshold for a period of time, it is considered that the first endpoint of the detection window that first exceeds the threshold begins to rise. When the feature quantity continues to exceed the threshold and gradually approaches and finally remains less than the threshold and remains stable, the end position of the detection window that last exceeded the threshold is considered to have ended the rise.

[0165] Based on this, the sliding detection window and the reference window can be used to obtain the characteristic quantity used to judge the movement state of the climbing frame, and based on the comparison between the characteristic quantity and the threshold value, 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, according to the monitoring rules corresponding to the current motion state, abnormal state monitoring of the target climbing frame based on the filtered data may include:

[0167] S511, based on the filtered data, using the CUSUM algorithm to count the deviation statistics of the sequence data relative to the preset reference value in real time;

[0168] S512. Determine whether the target climbing frame is in an abnormal state based on whether the offset statistic exceeds a preset warning threshold; wherein the warning threshold is determined based on a monitoring rule of the current motion state.

[0169] It should be noted that the CUSUM algorithm is a statistical tool for quality control, widely used in industry and engineering to detect small and continuous changes in the process. 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, while accumulation is the accumulation of these deviations over time. The CUSUM algorithm is very sensitive to small mean changes, is suitable for univariate and multivariate data, and can be analyzed retrospectively.

[0170] Based on this, the embodiment of the present application can use the CUSUM algorithm to count the characteristic quantities used to monitor the abnormal state of the climbing frame. Specifically, first define the CUSUM statistic when the upward deviation occurs:

[0171]

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

[0173] Similarly, the cumulative sum of downward shifts 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 brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method, and will not be described in detail here.

[0186] like Figure 5 As 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 in the memory 510 and executable on the processor 520, wherein the processor 520 reads the program from the memory 510 through a bus 530 and executes the program to implement a method of any embodiment included in the above-mentioned method for monitoring the state of a building climbing frame.

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

[0188] The memory 510 may be used to store instructions executed by the processor 520 or data related to the execution of instructions. These instructions and / or data may include codes for implementing some or all functions of one or more modules described in the embodiments of the present application. The processor 520 of the disclosed embodiment may be used to execute 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 memory known to those skilled in the art.

[0189] Some embodiments of the present application further provide a computer-readable storage medium having a computer program stored thereon. The computer program is executed by a processor to execute the method described in the method embodiment.

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

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

[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 schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a 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 box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

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

[0194] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0195] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0196] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0197] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for monitoring the state of a building climbing frame, characterized in that: include: Obtain satellite positioning monitoring data of the target climbing frame; Taking the preset data volume as the processing window, dynamically identifying the deformation of the target climbing frame within the current processing window based on the satellite positioning monitoring data, and removing gross errors from the satellite positioning monitoring data of the current processing window in combination with the deformation; Perform data interpolation on the satellite positioning monitoring data after gross error elimination to obtain preprocessed data; Based on the pre-constructed observation system discrete model corresponding to the target climbing frame, the pre-processed data is filtered in combination with a preset robust Kalman filter model to obtain filtered data; The current motion state of the target climbing frame is identified in real time based on the filtered data, and according to the monitoring rules corresponding to the current motion state, the target climbing frame is monitored for abnormal conditions based on the filtered data.

2. The state monitoring method of a building climbing frame according to claim 1 is characterized in that: The method uses a preset data volume as a processing window, dynamically identifies the deformation of the target climbing frame within the current processing window based on the satellite positioning monitoring data, and removes gross errors from the satellite positioning monitoring data of the current processing window in combination with the deformation, including: Taking a preset amount of data as a processing window, based on the satellite positioning monitoring data, and using the Laida criterion to determine a confidence interval corresponding to the current processing window; Determining whether the target climbing frame is deformed within the current processing window based on the confidence interval; In the case that the target climbing frame does not deform, the satellite positioning monitoring data of the current processing window is subjected to gross error elimination based on the confidence interval; In the case where the target climbing frame is deformed, the confidence interval is adjusted, and gross errors of the satellite positioning monitoring data in the current processing window are eliminated based on the adjusted confidence interval.

3. The state monitoring method of a building climbing frame according to claim 2 is characterized in that: The determining, based on the confidence interval, whether the target climbing frame is deformed within the current processing window includes: Based on the confidence interval, identifying gross error data within a current processing window from the satellite positioning monitoring data, and counting the corresponding gross error data amount; Whether the target climbing frame is deformed within the current processing window is determined based on whether the amount of gross error data exceeds a preset data amount threshold.

4. The state monitoring method of a building climbing frame according to claim 1 is characterized in that: The pre-constructed observation system discrete model corresponding to the target climbing frame is combined with a preset robust Kalman filter model to filter the pre-processed data to obtain filtered data, including: Obtain a pre-constructed observation system discrete 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; The preprocessed data is filtered in combination with the observation system discrete model and a preset robust Kalman filter model to obtain filtered data.

5. The state monitoring method of a building climbing frame according to claim 1 is characterized in that: The pre-constructed observation system discrete model corresponding to the target climbing frame is combined with a preset robust Kalman filter model to filter the pre-processed data to obtain filtered data, including: In the prediction and updating process of the robust Kalman filter model, a height constraint is imposed on the predicted value of each iteration based on a preset height constraint condition; Among them, in a preset initial monitoring period, a preset height threshold is used as the height constraint condition; in the remaining monitoring periods except the initial monitoring period, real-time calculation is performed based on a preset angle threshold and the corresponding height constraint condition is determined.

6. The state monitoring method of a building climbing frame according to claim 1 is characterized in that: The real-time identification of the current motion state of the target climbing frame based on the filtered data includes: Based on the filtered data, the means of the corresponding sequence points in each window are calculated in real time 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 overlap length; The difference between the sequence point means 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 relationship between the difference within a preset time period and a preset threshold.

7. The state monitoring method of a building climbing frame according to claim 1 is characterized in that: The abnormal state monitoring of the target climbing frame based on the filtered data according to the monitoring rules corresponding to the current motion state includes: Based on the filtered data, the CUSUM algorithm is used to calculate the deviation statistics of the sequence data relative to the preset reference value in real time; Whether the target climbing frame is in an abnormal state is determined based on whether the offset statistic exceeds a preset warning threshold; wherein the warning threshold is determined based on a monitoring rule of the current motion state.

8. An electronic device, characterized in that: It comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor can implement the state monitoring method of the building climbing frame as described in any one of claims 1 to 7 when executing the program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the state monitoring method of the building climbing frame as described in any one of claims 1 to 7 is executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the state monitoring method of the building climbing frame described in any one of claims 1 to 7 is implemented.

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