Ferris wheel rim friction ring deviation detection method and system

Abstract

The present invention relates to the field of data processing technology, and proposes a Ferris wheel rim friction ring deviation detection method and system, including: collecting displacement monitoring data of multiple groups of displacement sensors and multi-dimensional related data; segmenting the displacement monitoring data to obtain a number of displacement data segments; obtaining the abnormal possible factor of each displacement monitoring data in each displacement data segment; obtaining the relevant change curve of each displacement data segment in each dimension; obtaining the influence of each dimension on each displacement monitoring data in each displacement data segment; adjusting the abnormal possible factor of each displacement monitoring data to obtain the abnormal determination factor, and then obtaining the displacement monitoring data to be adjusted; obtaining the adjusted runout deviation monitoring data; and performing runout deviation detection on the Ferris wheel rim friction ring. The present invention aims to solve the problem that multi-dimensional environmental data will affect the friction and pressure on the Ferris wheel rim friction ring, and reduce the accuracy of runout deviation monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for detecting deviation of a rim friction ring of a Ferris wheel. Background Art

[0002] Whether the runout deviation of the rim friction ring of the Ferris wheel meets the standard is an important indicator of whether the Ferris wheel can operate normally, and it is also related to whether the Ferris wheel can operate safely. It needs to meet the national standards. The drive of the Ferris wheel usually adopts friction drive. The motor drives the rim friction wheel to rotate through the transmission system. The friction wheel is pressed against the raceway of the rim friction ring (turntable drive ring) by the corresponding devices (such as springs and connecting rods). The friction wheel rotates the friction ring through friction force, thereby driving the entire turntable to rotate. In this process, the end face circle runout deviation of the friction ring on the turntable can reflect the operating status of the Ferris wheel. It needs to be monitored in time to avoid exceeding the relevant standards and affecting the safe operation of the Ferris wheel.

[0003] In the prior art, the displacement of the spring clamping device in the drive unit is detected by multiple groups of displacement sensors, and the displacement of the rim friction ring of the turntable under the constraint condition of the drive unit is analyzed to detect the runout deviation of the rim friction ring. However, the displacement of the spring clamping device in the drive unit is affected by many environmental factors in addition to the deviation caused by the movement of the Ferris wheel itself. For example, changes in wind speed will affect the clamping of the rim friction ring, and high humidity will reduce the friction of the raceway on the rim friction ring. The load change of the Ferris wheel will also affect the pressure generated by the clamping of the rim friction ring, thereby affecting the data monitoring of the displacement sensor. Summary of the invention

[0004] The present invention provides a method and system for detecting deviation of a Ferris wheel rim friction ring, so as to solve the problem that the existing multi-dimensional environmental data may affect the friction and pressure on the Ferris wheel rim friction ring, thereby reducing the accuracy of displacement monitoring. The technical scheme adopted is as follows:

[0005] The present invention proposes a method for detecting deviation of a Ferris wheel rim friction ring, the method comprising the following steps:

[0006] Collect displacement monitoring data and multi-dimensional related data of multiple groups of displacement sensors, and construct a displacement monitoring sequence for each group of displacement sensors;

[0007] Based on the amplitude change of the displacement monitoring data in time series, the displacement monitoring sequence is segmented to obtain several displacement data segments; based on the similarity relationship between the displacement data segments and their length differences, the possible abnormal factors of each displacement monitoring data in each displacement data segment are obtained; the trend item data of the displacement monitoring data is obtained, and the relevant data of several displacement data segments and each dimension are combined to obtain the relevant change curve of each displacement data segment in each dimension;

[0008] According to the change of the correlation coefficient in the correlation change curve of the displacement data segment in each dimension, the influence degree of each dimension on each displacement monitoring data in each displacement data segment is obtained; the abnormal possibility factor of each displacement monitoring data is adjusted to obtain the abnormal determination factor, and then the displacement monitoring data to be adjusted is obtained; based on the trend item data corresponding to the displacement monitoring data to be adjusted, combined with the correlation change curve and the influence degree, the adjusted runout deviation monitoring data is obtained;

[0009] Based on the runout deviation monitoring data adjusted by multiple sets of displacement sensors, the runout deviation of the Ferris wheel rim friction ring is detected.

[0010] Optionally, the method of segmenting the displacement monitoring sequence to obtain a plurality of displacement data segments based on the amplitude change in the displacement monitoring data time series includes the following specific methods:

[0011] Acquire several peaks and troughs of the displacement monitoring sequence; perform STL decomposition on the displacement monitoring sequence to obtain a trend item sequence, a period item sequence and a residual item sequence of the displacement monitoring sequence, wherein the trend item sequence includes the trend item data of each displacement monitoring data, the period item sequence includes the period item data of each displacement monitoring data, and the residual item sequence includes the residual item data of each displacement monitoring data; acquire the amplitude mean of all period item data in the period item sequence as the period item mean; and acquire the amplitude mean of several peaks of the displacement monitoring sequence as the peak mean; and the amplitude mean of several troughs as the trough mean; acquire the absolute value of the difference between the peak mean and the period item mean, and the absolute value of the difference between the trough mean and the period item mean, and use the amplitude type corresponding to the minimum value of the two difference absolute values ​​as the determination extreme value;

[0012] Perform DBSCAN clustering on all the determined extreme values, and use the absolute value of the difference between the amplitudes of the determined extreme values ​​as the distance metric to obtain several clusters; for any cluster, obtain the average of the absolute values ​​of the difference between the amplitudes of all the determined extreme values ​​in the cluster and the mean of the periodic term, and use it as the deviation mean of the cluster;

[0013] The several determined extreme values ​​in the clusters corresponding to the maximum deviation mean values ​​in all clusters are taken as analysis extreme values; the moment corresponding to each analysis extreme value is taken as a segmentation point, and the displacement monitoring sequence is segmented to obtain several displacement data segments.

[0014] Optionally, the specific method for obtaining the possible abnormal factors of each displacement monitoring data in each displacement data segment is as follows:

[0015] Obtain the number of displacement monitoring data in each displacement data segment and use it as the segment length of each displacement data segment; calculate the DTW distance for any two displacement data segments. Possible abnormal factors of displacement data segments The calculation method is:

[0016]

[0017] in, Indicates The length of the displacement data segment, represents the mean length of all displacement data segments, Indicates the maximum absolute value of the difference between the segment length of all displacement data segments and the mean segment length, Indicates the number of displacement data segments, Indicates The displacement data segment and DTW distance of displacement data segments; represents the absolute value function, represents an exponential function with a natural constant as base;

[0018] The first The possible abnormal factors of the displacement data segment are taken as the The possible abnormal factors of each displacement monitoring data in the displacement data segment.

[0019] Optionally, the obtaining of the relevant change curve of each displacement data segment in each dimension includes the following specific methods:

[0020] Based on the trend item data of displacement monitoring data, obtain the The trend item data segment corresponding to the displacement data segment; and based on the Several moments corresponding to the displacement data segments are obtained The related data segments of each displacement data segment in each dimension;

[0021] For The displacement data segment of the dimension corresponds to the trend item data segment and in The relevant data segments of each dimension are traversed from the first data point one by one, and the Pearson correlation coefficient is calculated for the data points that have been traversed in the trend item data segment and the relevant data segment;

[0022] The horizontal axis is the order value of the data points, and the vertical axis is the Pearson correlation coefficient. The displacement data segment is in The relevant change curves under different dimensions.

[0023] Optionally, the degree of influence of each dimension on each displacement monitoring data in each displacement data segment is specifically obtained by:

[0024] According to the difference in correlation coefficients corresponding to adjacent displacement monitoring data in the correlation change curve, the influence factor of each dimension on the change of each displacement monitoring data in each displacement data segment is obtained;

[0025] According to the change of the correlation coefficient corresponding to the adjacent displacement monitoring data in the correlation change curve and the previous correlation coefficient, the response influence factor of each dimension on each displacement monitoring data in each displacement data segment is obtained;

[0026] For Remove the trend item data segment of the displacement data segment The trend item data corresponding to the displacement monitoring data is The displacement data segment is in Remove the first The Pearson correlation coefficient is calculated for the trend item data segment after removal and the related data segment after removal as the first The dimension for In the displacement data segment The de-centering correlation coefficient of displacement monitoring data;

[0027]

[0028] in, Indicates The dimension for In the displacement data segment The impact of displacement monitoring data, Indicates The trend item data segment of the displacement data segment is related to the The Pearson correlation coefficient of the relevant data segments of the dimensions, Indicates The dimension for In the displacement data segment The de-centering correlation coefficient of displacement monitoring data is Indicates The dimension for In the displacement data segment The influence factors of displacement monitoring data change, Indicates The dimension for In the displacement data segment The response influencing factor of displacement monitoring data; represents the absolute value function.

[0029] Optionally, the specific method of obtaining the influence factor of each dimension on the change of each displacement monitoring data in each displacement data segment includes:

[0030]

[0031] in, Indicates The dimension for In the displacement data segment The influence factors of displacement monitoring data change, and Respectively represent The displacement data segment is in The relevant change curve under the dimension The Pearson correlation coefficient corresponding to the displacement monitoring data and the The Pearson correlation coefficient corresponding to the displacement monitoring data is Indicates The displacement monitoring data is in The order value in the displacement data segment, and Respectively represent The displacement data segment is in The relevant change curve under the dimension The Pearson correlation coefficient corresponding to the displacement monitoring data and the The Pearson correlation coefficient corresponding to the displacement monitoring data is Represents an exponential function with a natural constant as its base.

[0032] Optionally, the obtaining of the response influence factor of each dimension on each displacement monitoring data in each displacement data segment includes the following specific methods:

[0033]

[0034] in, Indicates The dimension for In the displacement data segment The influencing factor of the response of displacement monitoring data is and Respectively represent The displacement data segment is in The relevant change curve under the dimension The Pearson correlation coefficient corresponding to the displacement monitoring data and the The Pearson correlation coefficient corresponding to the displacement monitoring data is Indicates The displacement data segment is in The relevant change curves under the dimension end at The mean of the Pearson correlation coefficients of the displacement monitoring data and all its previous ones.

[0035] Optionally, the adjusting of the possible abnormality factors of each displacement monitoring data to obtain the abnormality determination factor includes the following specific methods:

[0036]

[0037] in, Indicates In the displacement data segment The abnormality determination factor of displacement monitoring data, Indicates In the displacement data segment The possible abnormal factors of displacement monitoring data are Indicates that all dimensions are In the displacement data segment The maximum impact degree of each displacement monitoring data.

[0038] Optionally, the step of obtaining the adjusted jitter deviation monitoring data includes the following specific methods:

[0039] For In the displacement data segment displacement monitoring data, which is the displacement monitoring data to be adjusted, and the adjustment process is: for the dimension, the The first trend item data in the trend item data segment of the displacement data segment to the The trend item data are input into the weighted least square method, and each trend item data is The displacement data segment is in The corresponding correlation coefficient in the correlation change curve under the dimension is used as the weight and output in the Dimension In the displacement data segment The predicted value of the trend item data; In the displacement data segment Correction value of trend item data The calculation method is:

[0040]

[0041] in, Indicates In the displacement data segment The value of the trend item data, Indicates the number of dimensions, Indicates The dimension for In the displacement data segment The impact of displacement monitoring data, Indicates Dimension In the displacement data segment The predicted value of the trend item data;

[0042] Obtain the correction value of the trend item data of each displacement monitoring data to be adjusted, combine it with the trend item data of the displacement monitoring data that does not need to be adjusted, get a corrected trend item sequence, combine it with the periodic item sequence and the residual item sequence, reconstruct it through the STL decomposition inverse transform, get a corrected displacement monitoring sequence, and record it as a jitter deviation monitoring sequence, wherein the jitter deviation monitoring sequence includes a number of jitter deviation monitoring data.

[0043] The present invention also proposes a Ferris wheel rim friction ring deviation detection system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the above method when executing the computer program.

[0044] The beneficial effects of the present invention are as follows: the present invention analyzes the displacement data collected by the spring clamping device on the rim friction ring of the Ferris wheel, and corrects the runout deviation in combination with environmental factors such as wind speed, humidity and load, thereby reducing the interference of changes in multi-dimensional environmental factors on the runout deviation of the rim friction ring of the Ferris wheel; wherein the extreme value moments for segmenting the displacement monitoring sequence are selected according to the characteristics of the periodic motion of the Ferris wheel, and a plurality of displacement data segments are obtained, and the abnormal possible factors of each displacement data segment are preliminarily quantified through the characteristics that the displacement data segments usually have similar change modes and similar lengths, reflecting whether it is an abnormal change mode and does not conform to periodic changes; based on multi-dimensional phase The relevant data does not have the characteristics of periodic changes, and the correlation change analysis is carried out with the trend item data segment of the displacement data segment, so as to provide a basis for the subsequent quantification of the impact of each dimension on the displacement data segment; by analyzing the correlation change curve constructed by the trend item data of the multi-dimensional environmental factors on the displacement monitoring data, the degree of influence is quantified, and the abnormal possible factors are corrected based on the degree of influence to obtain the abnormal judgment factor, so as to retain the real abnormal runout deviation, and adjust the displacement monitoring data under the interference of multi-dimensional environmental factors, so as to ensure that the final runout deviation monitoring data is less affected by the changes in environmental factors, and can more truly reflect the runout deviation of the Ferris wheel rim friction ring. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0046] Figure 1 A schematic flow chart of a method for detecting deviation of a ferris wheel rim friction ring provided by one embodiment of the present invention;

[0047] Figure 2 is a schematic diagram of a displacement sensor in one embodiment of the present invention;

[0048] Figure 3 The figure is an example diagram of a displacement timing variation curve in one embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] See also Figure 1 , which shows a flow chart of a method for detecting deviation of a Ferris wheel rim friction ring provided by an embodiment of the present invention, the method comprising the following steps:

[0051] Step S001: Collect displacement monitoring data and multi-dimensional related data of multiple groups of displacement sensors, and construct a displacement monitoring sequence for each group of displacement sensors.

[0052] The purpose of this embodiment is to monitor and analyze the runout deviation of the rim friction ring of the Ferris wheel through the displacement of the spring clamping device detected by the displacement sensor, and at the same time, during the monitoring and analysis process, analyze the influence of environmental factors such as temperature, humidity and wind speed on the friction force on the rim friction ring, as well as the pressure generated by the Ferris wheel load change on the rim friction ring compression, so as to avoid the influence of multi-dimensional environmental data on the runout deviation of the Ferris wheel rim friction ring.

[0053] Specifically, Figure 2As shown in the figure, it shows a schematic diagram of the structure of the displacement sensor, the roller contacts the rim friction ring of the Ferris wheel, the roller is mounted on the roller mounting seat, and the spring clamping device is driven to move by the change of the contact rim friction ring, and the movement of the spring clamping device will change the length of the cable, and then the displacement amplitude is monitored by the cable sensor, and the rear of the displacement sensor is fixed on the driving unit of the Ferris wheel. By arranging several groups of displacement sensors, the runout deviation of the rim friction ring during the operation of the Ferris wheel is collected, and the collection starts when the Ferris wheel starts to run, and the sampling interval is set to 1 second as the collected displacement data; as shown in the figure Figure 3 As shown, it shows a schematic diagram of the time series change curve of the displacement data; all the displacement data are digitized, and after processing, the displacement monitoring data of multiple groups of displacement sensors are obtained, and several displacement monitoring data of the same group of displacement sensors are combined in a chronological order to form a displacement monitoring sequence of the group of displacement sensors; wherein the digitization processing is to perform linear normalization processing on all data of the same type.

[0054] Furthermore, the wind speed data and humidity data are acquired by using a wind speed sensor and a humidity sensor, and the sampling interval and the start time are the same as the displacement data; the mass of each car when it passes the lowest point of the Ferris wheel during the operation of the Ferris wheel is acquired, and the mass of the car at the highest point of the Ferris wheel at the same time is recorded, and the car bottom mass curve and the car top mass curve are constructed according to the time series distribution and the corresponding mass, respectively, wherein the horizontal axis is the time distribution with the same sampling interval, the data is updated each time the car passes, and then the data is kept unchanged until the next car passes, and the vertical axis is the car mass, and the time series data of the car bottom mass and the car top mass are obtained according to the car bottom mass curve and the car top mass curve; the collected wind speed data, humidity data, and the time series data of the car bottom mass and the car top mass are all digitized to obtain multi-dimensional related data; and the displacement monitoring data of any group of displacement sensors is subsequently analyzed and processed as an example.

[0055] It should be noted that the rotation of the Ferris wheel is periodic, and the displacement caused by the runout deviation will also remain periodic in timing with the rotation of the Ferris wheel. The abnormal runout deviation will lose the periodic change and form a local change pattern. It is necessary to extract the local change pattern based on the periodic change; and perform a correlation analysis between the local change pattern and the multidimensional related data, and obtain the degree of influence of the multidimensional related data on the local change pattern based on the correlation change, that is, whether the periodic change or abnormal runout deviation of this part is related to changes such as wind speed, load and humidity. The runout deviation detection process should remove this environmental interference and make adjustments based on the degree of influence, so as to obtain accurate runout deviation detection results of the Ferris wheel rim friction ring.

[0056] Step S002: based on the amplitude change of the displacement monitoring data in time series, the displacement monitoring sequence is segmented to obtain a number of displacement data segments; based on the similarity relationship between the displacement data segments and the length difference thereof, the possible abnormal factors of each displacement monitoring data in each displacement data segment are obtained; the trend item data of the displacement monitoring data is obtained, and the relevant data of the several displacement data segments and each dimension are combined to obtain the relevant change curve of each displacement data segment in each dimension.

[0057] Preferably, in one embodiment of the present invention, based on the amplitude change of the displacement monitoring data time series, the displacement monitoring sequence is segmented to obtain a plurality of displacement data segments, including the specific method of:

[0058] It should be noted that due to the operation cycle of the Ferris wheel itself, the runout deviation of the rim friction ring will also show periodic changes, which is manifested in the displacement monitoring data as a periodic change pattern. Therefore, it is necessary to divide the displacement monitoring data into data segments. In the process of periodic changes, each cycle will have an extreme value with a similar amplitude, such as Figure 3 Shown are several minimum values ​​in the variation curve, which are the minimum runout deviations during one cycle of the Ferris wheel. This minimum runout deviation is universal, that is, it will appear in each cycle of the Ferris wheel. Using this as the basis for period division can ensure that the obtained displacement data segments are relatively accurate.

[0059] Specifically, AMPD (automatic multi-scale peak finding algorithm) is used to obtain several peaks of the displacement monitoring sequence, and the displacement monitoring sequence is axially symmetric about the horizontal axis, and several troughs are obtained by using the AMPD algorithm based on the symmetrical curve, so as to obtain several peaks and troughs of the displacement monitoring sequence, wherein the AMPD algorithm is an existing algorithm and will not be described in detail in this embodiment; the displacement monitoring sequence is subjected to STL decomposition to obtain a trend item sequence, a period item sequence and a residual item sequence of the displacement monitoring sequence, wherein the trend item sequence includes the trend item data of each displacement monitoring data, the period item sequence includes the period item data of each displacement monitoring data, and the residual item sequence includes the residual item data of each displacement monitoring data, wherein the ST The L time series decomposition algorithm is an existing algorithm and will not be described in detail in this embodiment; the amplitude mean of all periodic item data in the periodic item sequence is obtained as the periodic item mean; and the amplitude mean of several peaks of the displacement monitoring sequence is obtained respectively as the peak mean; and the amplitude mean of several troughs is obtained as the trough mean; the absolute value of the difference between the peak mean and the periodic item mean, and the absolute value of the difference between the trough mean and the periodic item mean are obtained respectively, and the amplitude type corresponding to the minimum value of the two difference absolute values ​​is used as the judgment extreme value, that is, if the absolute value of the difference corresponding to the trough mean is the smallest, the amplitude of the trough type is used as the judgment extreme value, and similarly, if the absolute value of the difference corresponding to the peak mean is the smallest, the amplitude of the peak type is used as the judgment extreme value.

[0060] It should be noted that since the displacement monitoring data may contain abnormal jitter deviations, and the periodic change pattern requires that such jitter deviations be extracted rather than directly eliminated, it is necessary to divide the data segments based on the displacement monitoring data. At the same time, the periodic item data can eliminate the impact of jitter deviations caused by other changes, and can directly reflect the periodic change pattern of the jitter deviation, that is, the periodic motion pattern of the Ferris wheel itself. The peaks and troughs are judged based on the amplitude mean of the periodic item data. The greater the difference between the amplitude mean of the peaks or troughs and the amplitude mean of the periodic item data, the more obvious the periodic change pattern of the corresponding type of data segment divided as the judgment extreme value will be, and the more consistent it will be with the periodic motion pattern of the Ferris wheel itself.

[0061] Furthermore, DBSCAN clustering is performed on all determined extreme values, and the distance measurement uses the absolute value of the difference between the amplitudes of the determined extreme values ​​to obtain several clusters; for any cluster, the mean of the absolute values ​​of the difference between the amplitudes of all determined extreme values ​​in the cluster and the mean of the periodic term is obtained, and used as the deviation mean of the cluster; several determined extreme values ​​in the cluster corresponding to the maximum deviation mean in all clusters are taken as analysis extreme values; the moment corresponding to each analysis extreme value is taken as a segmentation point, and the displacement monitoring sequence is segmented to obtain several displacement data segments; the first moment is also taken as a segmentation point, and the displacement monitoring data corresponding to each segmentation point is the first displacement monitoring data in each displacement data segment.

[0062] Preferably, in one embodiment of the present invention, based on the similarity relationship between the displacement data segments and the length difference thereof, the possible abnormal factor of each displacement monitoring data in each displacement data segment is obtained, including the specific method of:

[0063] Obtain the number of displacement monitoring data in each displacement data segment and use it as the segment length of each displacement data segment; calculate the DTW distance for any two displacement data segments, then Possible abnormal factors of displacement data segments The calculation method is:

[0064]

[0065] in, Indicates The length of the displacement data segment, represents the mean length of all displacement data segments, Indicates the maximum absolute value of the difference between the segment length of all displacement data segments and the mean segment length, Indicates the number of displacement data segments, Indicates The displacement data segment and DTW distance of displacement data segments; represents the absolute value function, represents an exponential function with a natural constant as the base. In this embodiment, Model to present inverse proportional relationship and normalization, As the input of the model, the implementer can set the inverse proportional function and normalization function according to the actual situation.

[0066] It should be noted that the possible abnormal factors are judged by the difference in segment length and the similarity between data changes. The fixed rotation period of the Ferris wheel will make the segment lengths of the displacement data segments divided according to the period basically similar. In normal circumstances, the segment length difference will be small, and the abnormal jitter deviation will destroy the normal periodic change pattern, resulting in changes in the segment length of the local displacement data segment. At the same time, the data change pattern will also change, thereby affecting the similarity between the data changes of different data segments. The greater the segment length difference and the greater the DTW distance between the displacement data segments and other displacement data segments, the greater the possible abnormal factor of the corresponding displacement data segment, that is, the displacement data segment itself does not conform to the normal periodic change pattern.

[0067] Furthermore, The abnormal possibility factor of the displacement data segment is assigned to the For each displacement monitoring data in the displacement data segment, the first The possible abnormal factors of each displacement monitoring data in the displacement data segment.

[0068] Preferably, in one embodiment of the present invention, the trend item data of the displacement monitoring data is obtained, and the relevant data of several displacement data segments and each dimension are combined to obtain the relevant change curve of each displacement data segment in each dimension, including the specific method of:

[0069] It should be noted that the trend item data of the displacement monitoring data can reflect the change pattern of the runout deviation itself without being affected by periodic changes, while the multi-dimensional related data usually do not have periodic changes. It is necessary to analyze the trend item data itself and the multi-dimensional related data. On the basis of each displacement data segment, the correlation coefficient analysis is performed on the trend item data segment and the related data segment data point by data point, and the related change curve is quantified to measure the impact of changes such as wind speed, humidity and load on the runout deviation during the Ferris wheel operation cycle.

[0070] Specifically, based on the trend item data of the displacement monitoring data, obtain the The trend item data segment corresponding to the displacement data segment; and based on the Several moments corresponding to the displacement data segments are obtained The related data segments of the displacement data segments in each dimension; Take the dimension as an example, The displacement data segment of the dimension corresponds to the trend item data segment and in The related data segments of the dimension are traversed from the first data point one by one, and the Pearson correlation coefficient is calculated for the data points that have been traversed in the trend item data segment and the related data segment. For example, when traversing to the third data point, the Pearson correlation coefficient is calculated for the first three data points of the two data segments; the horizontal axis is the order value of the data point, and the vertical axis is the Pearson correlation coefficient, and the first The displacement data segment is in The relevant change curves under different dimensions.

[0071] At this point, the extreme moments used to segment the displacement monitoring data are screened based on the characteristics of the Ferris wheel's periodic motion, and several displacement data segments are obtained. The characteristics that the displacement data segments usually have similar change patterns and similar lengths are used to preliminarily quantify the possible abnormal factors of each displacement data segment, reflecting whether it is an abnormal change pattern and does not conform to periodic changes; based on the characteristic that there is no periodic change in multi-dimensional related data, a correlation change analysis is performed with the trend item data segment of the displacement data segment, thereby providing a basis for the subsequent quantification of the impact of each dimension on the displacement data segment.

[0072] Step S003, according to the change of the correlation coefficient in the relevant change curve of the displacement data segment in each dimension, obtain the influence of each dimension on each displacement monitoring data in each displacement data segment; adjust the abnormal possibility factor of each displacement monitoring data to obtain the abnormal judgment factor, and then obtain the displacement monitoring data to be adjusted; based on the trend item data corresponding to the displacement monitoring data to be adjusted, combined with the relevant change curve and the said influence degree, obtain the adjusted jitter deviation monitoring data.

[0073] Preferably, in one embodiment of the present invention, according to the change of the correlation coefficient in the correlation change curve of the displacement data segment under each dimension, the influence degree of each dimension on each displacement monitoring data in each displacement data segment is obtained, and the specific method includes:

[0074] It should be noted that in the correlation change curve constructed based on the trend item data segment and the related data segment, the trend item data segment reflects the data changes of the runout deviation apart from the periodic changes, that is, the trend item data reflects the changes in the pressure of the rim friction ring under the influence of wind speed, humidity and load during the rotation of the Ferris wheel. The related change curve is used to quantify the degree to which the pressure change occurring at the corresponding moment is affected by the related data of each dimension; if the correlation coefficient gradually increases with the increase of displacement monitoring data, the corresponding dimension has a greater impact on it, that is, it is in a normal change state; if the correlation coefficient changes suddenly with the increase of displacement monitoring data, and the correlation changes dramatically, the corresponding dimension has a smaller impact on the change of the runout deviation of the rim friction ring of this incident, and is more likely to be affected by other dimensions, so as to analyze the degree of influence of each dimension on the displacement monitoring data in the displacement data segment.

[0075] Specifically, The dimension for In the displacement data segment The calculation method of the influence degree of displacement monitoring data is:

[0076]

[0077] in, Indicates The dimension for In the displacement data segment The influence factors of displacement monitoring data change, and Respectively represent The displacement data segment is in The relevant change curve under the dimension The Pearson correlation coefficient corresponding to the displacement monitoring data and the The Pearson correlation coefficient corresponding to the displacement monitoring data is Indicates The displacement monitoring data is in The order value in the displacement data segment, and Respectively represent The displacement data segment is in The relevant change curve under the dimension The Pearson correlation coefficient corresponding to the displacement monitoring data and the Pearson correlation coefficient corresponding to displacement monitoring data; represents the absolute value function, represents an exponential function with a natural constant as the base. In this embodiment, Model to present inverse proportional relationship and normalization processing, As the input of the model, the implementer can set the inverse proportional function and normalization function according to the actual situation;

[0078]

[0079] in, Indicates The dimension for In the displacement data segment The influencing factor of the response of displacement monitoring data is and Respectively represent The displacement data segment is in The relevant change curve under the dimension The Pearson correlation coefficient corresponding to the displacement monitoring data and the The Pearson correlation coefficient corresponding to the displacement monitoring data is Indicates The displacement data segment is in The relevant change curves under the dimension end at The mean of the Pearson correlation coefficients of the displacement monitoring data and its previous ones; represents the absolute value function, represents an exponential function with a natural constant as the base. In this embodiment, Model to present inverse proportional relationship and normalization, As the input of the model, the implementer can set the inverse proportional function and normalization function according to the actual situation;

[0080] For Remove the trend item data segment of the displacement data segment The trend item data corresponding to the displacement monitoring data is The displacement data segment is in Remove the first The Pearson correlation coefficient is calculated for the trend item data segment after removal and the related data segment after removal as the first The dimension for In the displacement data segment The de-centering correlation coefficient of displacement monitoring data;

[0081]

[0082] in, Indicates The dimension for In the displacement data segment The impact of displacement monitoring data, Indicates The trend item data segment of the displacement data segment is related to the The Pearson correlation coefficient of the relevant data segments of the dimensions, Indicates The dimension for In the displacement data segment The de-centering correlation coefficient of displacement monitoring data is Indicates The dimension for In the displacement data segment The influence factors of displacement monitoring data change, Indicates The dimension for In the displacement data segment The response influencing factor of displacement monitoring data; represents the absolute value function.

[0083] It should be noted that the correlation coefficient change between the current displacement monitoring data and the previous adjacent displacement monitoring data is compared with the correlation coefficient change of the previous adjacent displacement monitoring data. The greater the difference in the correlation coefficient change, the smaller the relationship between the correlation coefficient change caused by the change in the corresponding trend item data and the current dimension, that is, the smaller the impact of the current dimension on the mutation, the smaller the change impact factor;

[0084] After the displacement monitoring data mutates, the subsequent correlation coefficient will recover, that is, the relevant data of the corresponding dimension and the trend item data are not affected by the mutation. By comparing with the previous correlation coefficient, the smaller the difference, the smaller the possibility of mutation, and the greater the impact of the corresponding dimension; on the contrary, the larger the difference, the more likely it is that a mutation unrelated to the corresponding dimension will occur, and the smaller the impact of the corresponding dimension, so as to quantify the recovery impact factor;

[0085] When comparing the correlation coefficient between the removed core and the data segments, the smaller the change in the correlation coefficient is, the less the removed displacement monitoring data is affected by the corresponding dimension, and the smaller the degree of influence is. The smaller the change influence factor and the recovery influence factor are, the smaller the degree of influence is.

[0086] Furthermore, the influence of each dimension on each displacement monitoring data in each displacement data segment is obtained according to the above method; in particular, for the first displacement monitoring data in each displacement data segment, the corresponding correlation coefficient in the relevant change curve under each dimension is directly used as the corresponding influence degree of each dimension.

[0087] Preferably, in one embodiment of the present invention, the abnormal possibility factor of each displacement monitoring data is adjusted to obtain the abnormal determination factor, and then the displacement monitoring data to be adjusted is obtained, and the specific method includes:

[0088] No. In the displacement data segment Abnormality determination factors of displacement monitoring data The calculation method is:

[0089]

[0090] in, Indicates In the displacement data segment The possible abnormal factors of displacement monitoring data are Indicates that all dimensions are In the displacement data segment The maximum impact degree of each displacement monitoring data.

[0091] It should be noted that if there is a larger influence degree among the influence degrees of each dimension corresponding to the abnormal jitter deviation, the possibility that it is the jitter deviation abnormality generated by itself is smaller, and the possibility of being interfered by other environmental factors is greater, and its abnormal possibility factor needs to be reduced; when the influence degrees of multiple dimensions are all small, the adjustment parameters will be larger, and the obtained abnormal determination factor will not be adjusted too much, that is, as a jitter deviation that may itself cause abnormality, it will still present a larger abnormal determination factor.

[0092] Furthermore, an abnormality determination threshold of 0.6 is preset, and the displacement monitoring data whose abnormality determination factor is less than the abnormality determination threshold is used as the displacement monitoring data to be adjusted.

[0093] Preferably, in one embodiment of the present invention, based on the trend item data corresponding to the displacement monitoring data to be adjusted, combined with the relevant change curve and the degree of influence, the adjusted runout deviation monitoring data is obtained, including the specific method of:

[0094] It should be noted that after screening the displacement monitoring data to be adjusted through the abnormal judgment factor, the displacement monitoring data generated by the real abnormal runout deviation does not need to be adjusted, and the displacement monitoring data to be adjusted needs to be predicted and adjusted based on its corresponding trend item data and its Pearson correlation coefficient with each dimension, so as to remove the influence of environmental factors such as wind speed and humidity and the load change of the Ferris wheel itself on the runout deviation of the rim friction ring, and then obtain the runout deviation monitoring data that can truly reflect the operating status of the Ferris wheel.

[0095] Specifically, for In the displacement data segment displacement monitoring data, which is the displacement monitoring data to be adjusted, then adjust its trend item data, the adjustment process is: for the dimension, the The first trend item data in the trend item data segment of the displacement data segment to the The trend item data are input into the weighted least square method, and each trend item data is The displacement data segment is in The corresponding correlation coefficient in the correlation change curve under the dimension is used as the weight and output in the Dimension In the displacement data segment The predicted value of the trend item data; the weighted least square method is a well-known technology and will not be described in detail in this embodiment; then In the displacement data segment Correction value of trend item data The calculation method is:

[0096]

[0097] in, Indicates In the displacement data segment The value of the trend item data, Indicates the number of dimensions, Indicates The dimension for In the displacement data segment The impact of displacement monitoring data, Indicates Dimension In the displacement data segment The predicted value of the trend item data.

[0098] It should be noted that, based on the predicted value, the difference between the original value and the predicted value is calculated, and the difference is weighted and averaged with the influence of the corresponding dimension on the displacement monitoring data as the weight, and then the correction value is obtained by subtracting the difference from the original value; ensuring that the greater the influence of the dimension, the more the deviation between the predicted value and the original value can be removed.

[0099] Furthermore, according to the above method, the correction value of the trend item data of each displacement monitoring data to be adjusted is obtained, and the trend item data of the displacement monitoring data that does not need to be adjusted is combined to obtain a corrected trend item sequence, which is combined with the periodic item sequence and the residual item sequence and reconstructed through the STL decomposition inverse transform to obtain a corrected displacement monitoring sequence, which is recorded as a jitter deviation monitoring sequence, and the jitter deviation monitoring sequence includes a number of jitter deviation monitoring data.

[0100] At this point, the correlation change curve constructed by the trend item data of the displacement monitoring data caused by multi-dimensional environmental factors is analyzed to quantify the degree of influence, and the abnormal possible factors are corrected based on the degree of influence to obtain the abnormal judgment factor, thereby retaining the real abnormal runout deviation and adjusting the displacement monitoring data under the interference of multi-dimensional environmental factors, so as to ensure that the final runout deviation monitoring data is less affected by the changes in environmental factors and can more truly reflect the runout deviation of the Ferris wheel rim friction ring.

[0101] Step S004: Based on the adjusted vibration deviation monitoring data of the multiple displacement sensors, the vibration deviation of the Ferris wheel rim friction ring is detected.

[0102] It should be noted that according to national standards, the runout deviation of the Ferris wheel's rim friction ring shall not exceed 1 / 1500 of the diameter of the drive ring (rim friction ring). Taking a Ferris wheel with a diameter of 45 meters as an example, the runout deviation in the obtained runout deviation monitoring data shall not exceed 30mm, that is, the extreme difference in the runout deviation monitoring data shall not exceed 30mm. If it exceeds 30mm, the runout deviation of the Ferris wheel rim friction ring does not meet the standard.

[0103] Specifically, according to the above method, the displacement monitoring data of multiple groups of displacement sensors are used to obtain the runout deviation monitoring data; the range is calculated for all the runout deviation monitoring data, the diameter of the monitored Ferris wheel is 45 meters, and the runout deviation judgment threshold is 30 mm. If the range is greater than the runout deviation judgment threshold, the runout deviation of the Ferris wheel rim friction ring does not meet the standard; if it is less than or equal to the runout deviation judgment threshold, the runout deviation of the Ferris wheel rim friction ring meets the standard.

[0104] At this point, by analyzing the displacement data collected by the spring clamping device on the Ferris wheel rim friction ring, combined with environmental factors such as wind speed, humidity and load, the runout deviation is corrected to reduce the interference of changes in multi-dimensional environmental factors on the runout deviation of the Ferris wheel rim friction ring.

[0105] Another embodiment of the present invention provides a Ferris wheel rim friction ring deviation detection system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the above method steps S001 to S004 are implemented.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting deviation of a Ferris wheel rim friction ring, characterized in that: The method comprises the following steps: Collect displacement monitoring data and multi-dimensional related data of multiple groups of displacement sensors, and construct a displacement monitoring sequence for each group of displacement sensors; Based on the amplitude change of the displacement monitoring data in time series, the displacement monitoring sequence is segmented to obtain several displacement data segments; based on the similarity relationship between the displacement data segments and their length differences, the possible abnormal factors of each displacement monitoring data in each displacement data segment are obtained; the trend item data of the displacement monitoring data is obtained, and the relevant data of several displacement data segments and each dimension are combined to obtain the relevant change curve of each displacement data segment in each dimension; According to the change of the correlation coefficient in the correlation change curve of the displacement data segment in each dimension, the influence degree of each dimension on each displacement monitoring data in each displacement data segment is obtained; the abnormal possibility factor of each displacement monitoring data is adjusted to obtain the abnormal determination factor, and then the displacement monitoring data to be adjusted is obtained; based on the trend item data corresponding to the displacement monitoring data to be adjusted, combined with the correlation change curve and the influence degree, the adjusted runout deviation monitoring data is obtained; Based on the runout deviation monitoring data adjusted by multiple sets of displacement sensors, the runout deviation of the Ferris wheel rim friction ring is detected.

2. A method for detecting deviation of a Ferris wheel rim friction ring according to claim 1, characterized in that: The method of segmenting the displacement monitoring sequence to obtain a plurality of displacement data segments based on the amplitude change in the displacement monitoring data time series includes: Acquire several peaks and troughs of the displacement monitoring sequence; perform STL decomposition on the displacement monitoring sequence to obtain a trend item sequence, a period item sequence and a residual item sequence of the displacement monitoring sequence, wherein the trend item sequence includes the trend item data of each displacement monitoring data, the period item sequence includes the period item data of each displacement monitoring data, and the residual item sequence includes the residual item data of each displacement monitoring data; acquire the amplitude mean of all period item data in the period item sequence as the period item mean; and respectively obtaining the amplitude averages of several peaks of the displacement monitoring sequence as the peak average; and the amplitude mean of several troughs as the trough mean; respectively obtaining the absolute value of the difference between the peak mean and the periodic term mean, and the absolute value of the difference between the trough mean and the periodic term mean, and taking the amplitude type corresponding to the minimum value of the two difference absolute values ​​as the determination extreme value; Perform DBSCAN clustering on all the determined extreme values, and use the absolute value of the difference between the amplitudes of the determined extreme values ​​as the distance metric to obtain several clusters; for any cluster, obtain the average of the absolute values ​​of the difference between the amplitudes of all the determined extreme values ​​in the cluster and the mean of the periodic term, and use it as the deviation mean of the cluster; The several determined extreme values ​​in the clusters corresponding to the maximum deviation mean values ​​in all clusters are taken as analysis extreme values; the moment corresponding to each analysis extreme value is taken as a segmentation point, and the displacement monitoring sequence is segmented to obtain several displacement data segments.

3. A method for detecting deviation of a Ferris wheel rim friction ring according to claim 1, characterized in that: The specific method for obtaining the possible abnormal factors of each displacement monitoring data in each displacement data segment is as follows: Obtain the number of displacement monitoring data in each displacement data segment and use it as the segment length of each displacement data segment; calculate the DTW distance for any two displacement data segments. Possible abnormal factors of displacement data segments The calculation method is: in, Indicates The length of the displacement data segment, represents the mean length of all displacement data segments, Indicates the maximum absolute value of the difference between the segment length of all displacement data segments and the mean segment length, Indicates the number of displacement data segments, Indicates The displacement data segment and DTW distance of displacement data segments; represents the absolute value function, represents an exponential function with a natural constant as base; The first The possible abnormal factors of the displacement data segment are taken as the The possible abnormal factors of each displacement monitoring data in the displacement data segment.

4. A method for detecting deviation of a Ferris wheel rim friction ring according to claim 2, characterized in that: The specific method of obtaining the relevant change curve of each displacement data segment in each dimension includes: Based on the trend item data of displacement monitoring data, obtain the The trend item data segment corresponding to each displacement data segment; Based on the Several moments corresponding to the displacement data segments are obtained The related data segments of each displacement data segment in each dimension; For The displacement data segment of the dimension corresponds to the trend item data segment and in The relevant data segments of each dimension are traversed from the first data point one by one, and the Pearson correlation coefficient is calculated for the data points that have been traversed in the trend item data segment and the relevant data segment; The horizontal axis is the order value of the data points, and the vertical axis is the Pearson correlation coefficient. The displacement data segment is in The relevant change curves under different dimensions.

5. A method for detecting deviation of a Ferris wheel rim friction ring according to claim 4, characterized in that: The degree of influence of each dimension on each displacement monitoring data in each displacement data segment is specifically obtained by: According to the difference in correlation coefficients corresponding to adjacent displacement monitoring data in the correlation change curve, the influence factor of each dimension on the change of each displacement monitoring data in each displacement data segment is obtained; According to the change of the correlation coefficient corresponding to the adjacent displacement monitoring data in the correlation change curve and the previous correlation coefficient, the response influence factor of each dimension on each displacement monitoring data in each displacement data segment is obtained; For Remove the trend item data segment of the displacement data segment The trend item data corresponding to the displacement monitoring data is The displacement data segment is in Remove the first The relevant data of each displacement monitoring data at the corresponding moment; The Pearson correlation coefficient is calculated for the trend item data segment after removal and the correlation data segment after removal. The dimension for In the displacement data segment The de-centering correlation coefficient of displacement monitoring data; in, Indicates The dimension for In the displacement data segment The impact of displacement monitoring data, Indicates The trend item data segment of the displacement data segment is related to the The Pearson correlation coefficient of the relevant data segments of the dimensions, Indicates The dimension for In the displacement data segment The de-centering correlation coefficient of displacement monitoring data is Indicates The dimension for In the displacement data segment The influence factors of displacement monitoring data change, Indicates The dimension for In the displacement data segment The response influencing factor of displacement monitoring data; represents the absolute value function.

6. A method for detecting deviation of a Ferris wheel rim friction ring according to claim 5, characterized in that: The specific method of obtaining the influence factor of each dimension on the change of each displacement monitoring data in each displacement data segment includes: in, Indicates The dimension for In the displacement data segment The influence factors of displacement monitoring data change, and Respectively represent The displacement data segment is in The relevant change curve under the dimension The Pearson correlation coefficient corresponding to the displacement monitoring data and the The Pearson correlation coefficient corresponding to the displacement monitoring data is Indicates The displacement monitoring data is in The order value in the displacement data segment, and Respectively represent The displacement data segment is in The relevant change curve under the dimension The Pearson correlation coefficient corresponding to the displacement monitoring data and the The Pearson correlation coefficient corresponding to the displacement monitoring data is Represents an exponential function with a natural constant as its base.

7. A method for detecting deviation of a Ferris wheel rim friction ring according to claim 6, characterized in that: The specific method of obtaining the response influence factor of each dimension on each displacement monitoring data in each displacement data segment includes: in, Indicates The dimension for In the displacement data segment The influencing factor of the response of displacement monitoring data is and Respectively represent The displacement data segment is in The relevant change curve under the dimension The Pearson correlation coefficient corresponding to the displacement monitoring data and the The Pearson correlation coefficient corresponding to the displacement monitoring data is Indicates The displacement data segment is in The relevant change curves under the dimension end at The mean of the Pearson correlation coefficients of the displacement monitoring data and all its previous ones.

8. A method for detecting deviation of a Ferris wheel rim friction ring according to claim 1, characterized in that: The specific method of adjusting the possible abnormal factors of each displacement monitoring data to obtain the abnormal determination factor includes: in, Indicates In the displacement data segment The abnormality determination factor of displacement monitoring data is Indicates In the displacement data segment The possible abnormal factors of displacement monitoring data are Indicates that all dimensions are In the displacement data segment The maximum impact degree of each displacement monitoring data.

9. A method for detecting deviation of a Ferris wheel rim friction ring according to claim 2, characterized in that: The specific method of obtaining the adjusted jitter deviation monitoring data includes: For In the displacement data segment displacement monitoring data, which is the displacement monitoring data to be adjusted, and the adjustment process is: for the dimension, the The first trend item data in the trend item data segment of the displacement data segment to the The trend item data are input into the weighted least square method, and each trend item data is The displacement data segment is in The corresponding correlation coefficient in the correlation change curve under the dimension is used as the weight and output in the Dimension In the displacement data segment The predicted value of the trend item data; In the displacement data segment Correction value of trend item data The calculation method is: in, Indicates In the displacement data segment The value of the trend item data, Indicates the number of dimensions, Indicates The dimension for In the displacement data segment The impact of displacement monitoring data, Indicates Dimension In the displacement data segment The predicted value of the trend item data; Obtain the correction value of the trend item data of each displacement monitoring data to be adjusted, combine it with the trend item data of the displacement monitoring data that does not need to be adjusted, get a corrected trend item sequence, combine it with the periodic item sequence and the residual item sequence, reconstruct it through the STL decomposition inverse transform, get a corrected displacement monitoring sequence, and record it as a jitter deviation monitoring sequence, wherein the jitter deviation monitoring sequence includes a number of jitter deviation monitoring data.

10. A Ferris wheel rim friction ring deviation detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for detecting deviation of the rim friction ring of a Ferris wheel as claimed in any one of claims 1 to 9 are implemented.

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