A method for evaluating the drilling quality in bridge construction

Abstract

The present invention relates to the technical field of water conservancy construction monitoring, and specifically relates to a method for evaluating the drilling quality for bridge construction, including: obtaining the operation data during the water conservancy construction drilling process; performing STL decomposition on the operation data to obtain trend item data and residual item data, and obtaining the initial time series weight of each period; obtaining the trend anomaly degree of each period according to the difference between the distribution conditions of the trend item data in each period and the adjacent periods, the dispersion degree of the trend item data, and the difference of the trend item data; obtaining the residual anomaly degree of each period according to the dispersion degree of the residual item data in each period and the difference between the dispersion degrees of the residual item data; obtaining the preferred time series weight of each period, using the preferred time series weight to predict the operation data, and monitoring the water conservancy construction drilling process based on the prediction result. The data prediction result of the present invention is more accurate, thereby improving the accuracy of the drilling quality monitoring result.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy construction monitoring, and particularly relates to a method for evaluating the drilling quality for bridge construction. Background Art

[0002] Hydrogeological exploration is the science that studies the formation, distribution, recharge, and movement laws of water in water conservancy construction. Its purpose is to provide a scientific basis for the rational development and utilization of water resources in water conservancy construction. The drilling operation in water conservancy construction refers to creating holes in soil, rock, or other geological materials by using specific equipment, such as drills, hole punchers, etc. Hydrogeological exploration provides the necessary geological and hydrological information for water conservancy construction drilling, ensuring the safety, feasibility, and effectiveness of the project. By monitoring the operating parameters during the water conservancy construction drilling process, the construction quality can be ensured.

[0003] The existing method for monitoring the drilling quality in water conservancy construction is to predict the operating parameters during the drilling process through traditional prediction algorithms to predict the changes in the operating parameters in advance and achieve the purpose of early warning. However, the traditional prediction algorithms do not consider the abnormal operation problems of the drilling device and the influence of noise data during prediction, resulting in relatively inaccurate data prediction results, and further leading to a low accuracy of drilling quality evaluation. Summary of the Invention

[0004] In order to solve the technical problem that the data prediction results using the existing method are relatively inaccurate, resulting in a low accuracy of drilling quality evaluation, the purpose of the present invention is to provide a method for evaluating the drilling quality for bridge construction, and the specific technical solution adopted is as follows:

[0005] In a first aspect, the present invention provides a method for evaluating the drilling quality for bridge construction, and the method includes the following steps:

[0006] Obtain the operating data during the water conservancy construction drilling process within a set time period before the current moment;

[0007] Perform STL decomposition on the operating data to obtain the trend item data and residual item data in each period; according to the time interval between each period and the current moment, obtain the initial time series weight of each period;

[0008] According to the difference between the distribution of the trend item data in each period and the distribution of the trend item data in the adjacent period, the dispersion degree of the trend item data in each period, and the difference between the trend item data in each period and the trend item data in the adjacent period, obtain the trend anomaly degree of each period;

[0009] According to the degree of dispersion of the residual term data in each period and the difference between the degree of dispersion of the residual term data in each period and that in other periods, the degree of residual anomaly in each period is obtained;

[0010] According to the degree of trend anomaly, the degree of residual anomaly, and the initial time series weight, the optimal time series weight for each period is obtained. The operation data is predicted using the optimal time series weight, and the monitoring of the water conservancy construction drilling process is carried out based on the prediction result.

[0011] Preferably, obtaining the degree of trend anomaly for each period according to the difference between the distribution of the trend term data in each period and the distribution of the trend term data in the adjacent period, the degree of dispersion of the trend term data in each period, and the difference between the trend term data in each period and the adjacent period specifically includes:

[0012] Denote any one period as the target period. According to the difference between each trend term data in the target period and the adjacent trend term data, the data fluctuation degree of each trend term data in the target period is obtained, and the variance of the data fluctuation degrees of all trend term data in the target period is calculated to obtain the first characteristic coefficient;

[0013] Calculate the difference between the mean value and the median value of all trend term data in the target period as the trend distribution degree of the target period;

[0014] According to the difference between the trend distribution degrees of the target period and the adjacent period, and the difference between the mean values of the trend term data between the target period and other periods, the second characteristic coefficient is obtained;

[0015] According to the first characteristic coefficient and the second characteristic coefficient, the degree of trend anomaly of the target period is obtained, and both the first characteristic coefficient and the second characteristic coefficient are positively correlated with the degree of trend anomaly.

[0016] Preferably, the specific calculation formula of the second characteristic coefficient is:

[0017] ;

[0018] Wherein, represents the second characteristic coefficient of the target period, t represents the t-th period, represents the trend distribution degree of the t-th period, represents the trend distribution degree of the (t - 1)-th period, represents the (t + 1)-th trend distribution degree, N represents the total number of periods, represents the mean value of all trend term data within the t-th period, represents the mean value of all trend term data within the r-th period other than the t-th period.

[0019] Preferably, the calculation formula of the data fluctuation degree is specifically as follows:

[0020] ;

[0021] where represents the data fluctuation degree of the i-th trend item data in the t-th cycle, represents the i-th trend item data in the t-th cycle, represents the (i - 1)-th trend item data in the t-th cycle, represents the (i + 1)-th trend item data in the t-th cycle.

[0022] Preferably, obtaining the preferred timing weight of each cycle according to the trend anomaly degree, the residual anomaly degree, and the initial timing weight specifically includes:

[0023] For any cycle, use the residual anomaly degree in this cycle to correct the trend anomaly degree to obtain the corrected anomaly degree of this cycle; obtain the weight adjustment degree of this cycle according to the absolute value of the difference between the corrected anomaly degree and the trend anomaly degree, and there is a positive correlation between the absolute value of the difference and the weight adjustment degree;

[0024] Normalize the difference between the initial timing weight of this cycle and the weight adjustment degree to obtain the preferred timing weight of this cycle.

[0025] Preferably, using the residual anomaly degree in this cycle to correct the trend anomaly degree to obtain the corrected anomaly degree of this cycle specifically includes:

[0026] ;

[0027] where represents the corrected anomaly degree of the t-th cycle, represents the trend anomaly degree of the t-th cycle, represents the residual anomaly degree of the t-th cycle, represents the residual anomaly degree of the r-th cycle other than the t-th cycle, N represents the total number of cycles, and exp( ) represents the exponential function with the natural constant e as the base.

[0028] Preferably, obtaining the residual anomaly degree of each cycle according to the dispersion degree of the residual term data in each cycle and the difference between the dispersion degree of the residual term data in each cycle and that in other cycles specifically includes:

[0029] Denote any one period as the selected period. Based on the number of residual term data included in the selected period, the mean value of all residual term data, and the difference between each residual term data and the mean value of all residual term data, obtain the residual data characteristics of the selected period; based on the degree of balance of the difference between the residual data characteristics of the selected period and those of each other period, obtain the degree of residual abnormality of the selected period.

[0030] Preferably, the obtaining the degree of residual abnormality of the selected period based on the degree of balance of the difference between the residual data characteristics of the selected period and those of each other period specifically includes:

[0031] Calculate the ratio between the residual data characteristics of the selected period and any other period, and take the absolute value of the difference between 1 and the ratio as the difference characteristic between the selected period and the any other period; calculate the mean value of the difference characteristics between the selected period and each other period to obtain the degree of residual abnormality of the selected period.

[0032] Preferably, the calculation formula for the residual data characteristics of the selected period is specifically:

[0033] ;

[0034] Wherein, represents the residual data characteristics of the selected period, s represents the s-th period, represents the total number of residual term data included in the s-th period, represents the mean value of all residual term data in the s-th period, represents the u-th residual term data in the s-th period.

[0035] Preferably, the obtaining the initial timing weight of each period based on the time interval between each period and the current moment specifically includes:

[0036] For any one period, obtain the shortest time interval between the period and the current moment, and perform positive correlation normalization processing on the shortest time interval to obtain the initial timing weight of the period.

[0037] The embodiments of the present invention at least have the following beneficial effects:

[0038] The present invention first performs STL decomposition operation on the collected operation data to obtain data with different characterization properties, namely the trend item data and residual item data under each period, providing a data basis for subsequent reasonable and comprehensive analysis of the anomalies in the operation data. Moreover, through the time distribution of the time series data, an initial time series weight is obtained, which preliminarily characterizes the data proportion of the operation data for predictive analysis within each period. Then, the trend item data under each period is analyzed, fully considering the differences and dispersion degrees of the trend item data between adjacent periods, so that the obtained trend anomaly degree can reflect the comprehensive anomaly in the data change trend of the operation data within each period. Further, the residual item data under each period is analyzed, fully considering the dispersion degree of the residual item data within each period and the differences between the residual item data of adjacent periods, so that the obtained residual anomaly degree can reflect the anomalies in the residual items of the operation data within each period, and characterizes the possible degree of data anomalies caused by environmental factors in the operation data within each period. Finally, by combining the analysis results of the data anomaly degrees in the two aspects, the initial time series weight is corrected, which can fully consider the influence of the punching staggered row problem and noise data, making the data prediction result more accurate, and further improving the accuracy of the punching quality detection result. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0040] Figure 1 is a schematic flow chart of a method for evaluating the punching quality for bridge construction provided by the present invention;

[0041] Figure 2 is a distribution curve graph of the operation data within a set time period provided by an embodiment of the present invention;

[0042] Figure 3 is the seasonal item of the operation data provided by an embodiment of the present invention;

[0043] Figure 4 is the trend item of the operation data provided by an embodiment of the present invention;

[0044] Figure 5 is the residual item of the operation data provided by an embodiment of the present invention;

[0045] Figure 6 is the prediction result of the operation data provided by an embodiment of the present invention. Detailed implementation manners

[0046] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details a method for evaluating the drilling quality for bridge construction according to the present invention, including its specific implementation manners, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0048] As Figure 1 is a schematic flow chart of a method for evaluating the drilling quality for bridge construction.

[0049] Step 1: Perform STL decomposition on the operation data to obtain the trend item data and residual item data for each period; according to the time interval between each period and the current moment, obtain the initial time series weight for each period.

[0050] Due to the influence of the geological conditions at the drilling location, abnormal operations may occur during the water conservancy construction drilling process, such as abnormal vibrations. In severe cases, the direction of the drill bit may deviate, affecting the quality of the drilled holes. However, when existing prediction algorithms predict the vibration data during the water conservancy construction drilling process, there may be abnormal data in the historical vibration data that has been collected. Some of this abnormal data may be generated when the drilling equipment itself malfunctions, and this part of the data has certain value for predicting the operating conditions. While another part may be abnormal data under the influence of the external environment, that is, noise data caused by interference from objective environment and other factors. Using this part of the data to predict the operating conditions has no reference value.

[0051] In order to be able to more effectively process the changing trends and abnormal conditions of the operation data at multiple levels within a set time period, first perform STL decomposition on the operation data to obtain the trend item data and residual item data for each period. It should be noted that in this embodiment, the vibration data is used as the operation data, and the vibration data is taken as an example for illustration. The STL decomposition algorithm is a well-known technology. Performing STL decomposition operations on all operation data within a set time period can obtain the seasonal item, trend item, and residual item of the operation data. As Figure 2 shows the distribution schematic diagram of the operation data within a set time period. As Figure 3 shows the seasonal item of the operation data, that is, the periodic item. As Figure 4 shows the trend item of the operation data. AsFigure 5 Shown is the residual term of the operation data. By analyzing the seasonal term, each period of the operation data can be obtained, and then the corresponding trend term and residual term within the time period of each period can be obtained. Through Figure 3 It can be understood that the period in the example graph is 0.1.

[0052] Considering that during the drilling process of the water conservancy construction drilling device, there is also a certain degree of volatility in the operation data under normal operating conditions, and this data fluctuation phenomenon is allowed, random, and unpredictable. Since when predicting the data, the original data to be collected needs to have a certain degree of stability, that is, the data fluctuation is small. In order to improve the stability of the operation data, in this embodiment, by setting the time series weight, the collected operation data is weighted.

[0053] Based on this, first, a preliminary analysis is carried out on the time series distribution characteristics of the operation data within the set time period, and a basic initial time series weight can be obtained, that is, according to the time interval between each period and the current moment, the initial time series weight of each period is obtained. Specifically, for any period, the shortest time interval between the period and the current moment is obtained, and the shortest time interval is subjected to positive correlation normalization processing to obtain the initial time series weight of the period.

[0054] In this embodiment, taking any period as an example for illustration, the shortest time interval between the t-th period and the current moment can be the time interval between the moment corresponding to the last operation data within the t-th period and the current moment. Furthermore, the calculation formula for the initial time series weight of the t-th period can be expressed as:

[0055] ;

[0056] Among them, represents the initial time series weight of the t-th period, represents the current moment, represents the moment closest to the current moment within the t-th period, represents the shortest time interval between the t-th period and the current moment, and exp( ) represents the exponential function with the natural constant e as the base.

[0057] The shortest time interval between the t-th period and the current moment reflects the distance of the t-th period from the current moment in time series. The closer the time series distance, the more reference value the t-th period has for predicting the operation data at the next moment of the current moment. The data change trends between the two are more continuous in time series, and thus the corresponding value of the initial time series weight is larger.

[0058] is a preset influence factor. In this embodiment, the influence factor is determined by the time length within a set time period and is used to adjust the shortest time interval. The influence factor is specifically expressed as , represents the time length of the set time period. When the time length of the overall operation data is long, the change rate of the data should be slower, which can ensure that all the collected operation data within the set time period can participate in the prediction analysis process, that is, to a certain extent, the stability degree of the data is improved. Furthermore, the more data participates, the more accurate the overall prediction result is. When the time length corresponding to the overall operation data is shorter, the fluctuation degree of the data itself is relatively small, and the degree of adjustment using the time length can be appropriately reduced.

[0059] The setting of the influence factor can better reflect the characteristic that the closer to the current moment, the closer the similarity of the normal fluctuation of the data is, and the higher the overall prediction accuracy is. The initial time series weight reflects the weight ratio corresponding to the data of each cycle from the time distribution of each cycle, and further reflects the practical value of the data within the cycle for the prediction analysis process.

[0060] Step 2: Obtain the trend anomaly degree of each cycle according to the difference between the distribution of the trend item data in each cycle and the distribution of the trend item data in the adjacent cycle, the dispersion degree of the trend item data in each cycle, and the difference between the trend item data in each cycle and the adjacent cycle.

[0061] In the actual process of water conservancy construction drilling, the collected operation data inevitably contains abnormal operation data. When only considering the time distribution to weight and predict the operation data of each cycle, the influence of abnormal data is not considered, which may result in a low accuracy of the predicted data. In this implementation, by analyzing the distribution of the trend item data and the residual item data of each cycle respectively, and then evaluating the data anomaly degree of each cycle from two different characteristic data aspects, and finally combining the initial time series weight, the noise interference can be removed to the greatest extent, and the change characteristics of the data itself can be retained, making the prediction result more accurate.

[0062] First, the trend item data in each period reflects the change trend of the operation data within each period. The data change trend in each period may have a certain degree of abnormality, or may show abnormal conditions due to the influence of noise and differences. To analyze the abnormal conditions more accurately, the trend item data in each period is analyzed from multiple aspects. The first aspect is to analyze the degree of dispersion of the trend item data in each period. The second aspect is to analyze the difference between the distribution of the trend item data in each period and the distribution of the trend item data in the adjacent period. The third aspect is to analyze the difference between the trend item data in each period and the adjacent period, and the degree of trend abnormality in each period is obtained by combining the three aspects.

[0063] Based on this, first analyze the first aspect. Denote any period as the target period. According to the difference between each trend item data in the target period and the adjacent trend item data, obtain the data fluctuation degree of each trend item data in the target period, and calculate the variance of the data fluctuation degrees of all trend item data in the target period to obtain the first characteristic coefficient.

[0064] In this embodiment, take the t-th period as the target period and take any trend item data in the t-th period as an example for illustration. Then the calculation formula for the data fluctuation degree of the i-th trend item data in the target period can be expressed as:

[0065] ;

[0066] Among them, represents the data fluctuation degree of the i-th trend item data in the t-th period, represents the i-th trend item data in the t-th period, represents the (i - 1)-th trend item data in the t-th period, represents the (i + 1)-th trend item data in the t-th period.

[0067] reflects the data ratio between the i-th trend item data and the previous adjacent trend item data, reflects the data ratio between the i-th trend item data and the next adjacent trend item data. Furthermore, the data fluctuation degree reflects the difference change between the i-th trend item data and the two adjacent trend item data before and after it. The larger its value, the greater the difference between the i-th trend item data and the adjacent trend item data.

[0068] Then, calculate the variance of the data fluctuation degree of all trend item data in the target period to obtain the first characteristic coefficient. The first characteristic coefficient characterizes the fluctuation of the difference change of all trend item data corresponding to the target period within the adjacent data range. The larger the value of the first characteristic coefficient, the greater the fluctuation of the change trend of the trend item data in the target period, and the greater the degree of dispersion.

[0069] Then, conduct a joint analysis on the second aspect and the third aspect. Specifically, calculate the difference between the mean and the median of all trend item data in the target period as the trend distribution degree of the target period; based on the difference situation between the trend distribution degree of the target period and the adjacent period, as well as the difference situation of the mean of the trend item data between the target period and other periods, obtain the second characteristic coefficient.

[0070] In this embodiment, the calculation formula of the second characteristic coefficient of the t-th period, which is also the second characteristic coefficient of the target period, can be expressed as:

[0071] ;

[0072] Where, represents the second characteristic coefficient of the target period, t represents the t-th period, represents the trend distribution degree of the t-th period, represents the trend distribution degree of the (t - 1)-th period, represents the (t + 1)-th trend distribution degree, N represents the total number of periods, represents the mean of all trend item data within the t-th period, represents the mean of all trend item data within the r-th period other than the t-th period.

[0073] In this embodiment, for the t-th period, take the absolute value of the difference between the mean and the median of all trend item data within the t-th period as the trend distribution degree of the t-th period. Use the difference between the mean and the median of all data within a period to reflect the data distribution trend within this period. When the difference between the mean and the median is smaller, it indicates that the trend item data within the period is approximately distributed near the median, and further indicates that the trend item data within this period is more normal. When the difference between the mean and the median is larger, the data distribution is more discrete, indicating that the trend item data distribution within the period is more abnormal.

[0074] represents the difference situation of the trend distribution degree between the target period and its adjacent previous period, It represents the difference in the degree of trend distribution between the target period and the next adjacent period. The closer the ratio of the two is to 1, the closer the trend distribution changes of the target period in the adjacent periods are, and thus the smaller the possibility that the target period is abnormal, and the smaller the value of the corresponding second characteristic coefficient.

[0075] It represents taking the mean value of all trend item data in each period as the data unit, and performing discrete calculation with the mean value of the trend item data corresponding to the t-th period as the standard, which reflects the fluctuation between the mean value of the trend item data in the t-th period and the mean values of the trend item data in all other periods. The larger its value, the greater the degree of dispersion of the trend item data in the t-th period, and the greater the possibility of abnormality, and the larger the value of the second characteristic coefficient.

[0076] The first characteristic coefficient reflects the possibility of abnormality of the t-th period from the aspect of the fluctuation degree of the data trend distribution, and the second characteristic coefficient reflects the possibility of abnormality of the t-th period from the aspect of the data difference between adjacent periods and the degree of dispersion of the target period. The abnormality of the trend item data is evaluated by combining the characteristics of the trend item data of the target period in these aspects, that is, the trend abnormality degree of the target period is obtained according to the first characteristic coefficient and the second characteristic coefficient, and both the first characteristic coefficient and the second characteristic coefficient are positively correlated with the trend abnormality degree. In this embodiment, the normalized value of the product of the first characteristic coefficient and the second characteristic coefficient of the target period is used as the trend abnormality degree of the target period. The trend abnormality degree of the target period fully analyzes the distribution and change of the trend item data under the target period, and reflects the possibility of abnormality of the operation data in the target period.

[0077] It should be noted that in this embodiment, the change of the trend item data between each period and the two adjacent periods before and after it is analyzed, and at the same time, the change of each trend item data in each period with the two adjacent trend item data before and after it is also considered. Considering that there may be cases where the adjacent previous or adjacent subsequent data or period cannot be obtained, this embodiment does not consider the period for which adjacent data cannot be obtained, nor does it consider the trend item data for which adjacent data cannot be obtained in each period.

[0078] Step 3, obtain the residual abnormality degree of each period according to the degree of dispersion of the residual item data under each period and the difference in the degree of dispersion of the residual item data between each period and other periods.

[0079] The residual term data for each period obtained through STL decomposition represents the volatility and irregularity in the original data that cannot be captured by trends and seasonality. It contains more random data in the operating data, mostly due to random changes in the data affected by the external environment. Its overall distribution is similar to the Gaussian white noise distribution type. Based on this feature, in this embodiment, the characteristics of the residual term data within each period are quantified to describe the degree to which the operating data within each period is affected by external environmental factors.

[0080] Due to the unpredictability of the residual term data, the comprehensive discreteness calculation process of the residual term data for each period and the total number of residual term data can be used to characterize the data characteristics of the residual terms corresponding to each period. Furthermore, the similarity and difference in the data characteristics of the residual terms between each period and other periods are determined to identify the abnormal conditions of the residual terms for each period.

[0081] Based on this, according to the degree of discreteness of the residual term data for each period and the difference in the degree of discreteness of the residual term data between each period and other periods, the degree of residual abnormality for each period is obtained.

[0082] Specifically, first, any one period is denoted as the selected period. Based on the number of residual term data contained in the selected period, the mean of all residual term data, and the difference between each residual term data and the mean of all residual term data, the residual data characteristics of the selected period are obtained.

[0083] In this embodiment, when the s-th period is used as the selected period, the calculation formula for the residual data characteristics of the selected period can be expressed as:

[0084] ;

[0085] Among them, represents the residual data characteristics of the selected period, s represents the s-th period, represents the total number of residual term data contained in the s-th period, represents the mean of all residual term data in the s-th period, represents the u-th residual term data in the s-th period.

[0086] It reflects the difference between each residual term data and the mean value of the residual term data within the selected period, characterizing the fluctuation and dispersion degree of the residual term data in the selected period. Considering that the distribution equilibrium of the residual term data in each period is different, directly using variance to evaluate the dispersion degree is relatively absolute. In this embodiment, the difference between the fluctuation situation and the equilibrium situation of the residual term data is reflected by calculating the ratio of the dispersion degree to the mean value. At the same time, considering that the number of residual term data in each period may be different, the number of residual term data is used as a weight for further product calculation. The greater the corresponding data dispersion degree and the more the number of residual term data, the greater the value of the corresponding residual data feature, indicating that the data feature of the residual term in the selected period is more in line with the noise distribution. The residual data feature characterizes the degree to which the feature distribution of the residual term data in each period satisfies the noise distribution.

[0087] Furthermore, according to the equilibrium degree of the difference between the selected period and the residual data features of each other period, the residual abnormality degree of the selected period is obtained. More specifically, by calculating the ratio between the selected period and the residual data feature of any other period, the absolute value of the difference between 1 and the ratio is used as the difference feature between the selected period and the any other period; calculating the mean value of the difference features between the selected period and each other period to obtain the residual abnormality degree of the selected period.

[0088] In this embodiment, the calculation formula for the residual abnormality degree of the selected period can be expressed as:

[0089] ;

[0090] Wherein, represents the residual abnormality degree of the selected period, s represents the s-th period, N represents the total number of periods, represents the residual data feature of the s-th period, represents the residual data feature of the v-th period other than the s-th period.

[0091] represents the ratio between the selected period and the residual data features of other periods, is the difference feature between the selected period and the v-th other period. When the ratio is closer to 1, the value of the difference feature is smaller, indicating that the residual data features between the selected period and other periods are more similar, and the possibility of the existence of abnormality in the residual term of the selected period is smaller, and the value of the corresponding residual abnormality degree is smaller.

[0092] The degree of residual abnormality in each period reflects the likelihood of abnormal residual term data within the corresponding period. The larger the value of the degree of residual abnormality, the greater the likelihood of abnormal residual term data within the corresponding period, and thus the greater the likelihood that the abnormal situation of the operation data within that period is due to noise data caused by external environmental factors.

[0093] Step 4: Obtain the preferred time series weight for each period based on the degree of trend abnormality, the degree of residual abnormality, and the initial time series weight. Use the preferred time series weight to predict the operation data, and monitor the water conservancy construction drilling process based on the prediction results.

[0094] The degree of trend abnormality in each period can reflect the abnormal situation of the operation data in terms of trend within each period, and the degree of residual abnormality in each period can reflect the abnormal situation of the residual term data of the operation data within each period. When the value of the degree of residual abnormality is larger, it indicates that the greater the likelihood that the abnormal situation of the operation data in the period in terms of trend is due to abnormalities caused by external environmental factors, and thus it indicates that there may be noise data in the operation data of that period, and a smaller weight needs to be assigned to that period for data prediction analysis. When the value of the degree of residual abnormality is smaller, it indicates that the greater the likelihood that the abnormal situation of the operation data in the period in terms of trend is due to abnormalities during the drilling process, and a larger weight needs to be assigned to that period for data prediction analysis.

[0095] Based on this, it is necessary to finally obtain the preferred time series weight for each period by combining the degree of trend abnormality, the degree of residual abnormality, and the initial time series weight.

[0096] Specifically, first, use the degree of residual abnormality in each period to correct the degree of trend abnormality, so that the corrected degree of trend abnormality excludes the interference of noise and can truly represent the abnormal situation of the operation data in terms of trend within the period. That is, for any period, use the degree of residual abnormality in that period to correct the degree of trend abnormality to obtain the corrected degree of abnormality for that period.

[0097] In this embodiment, taking the t-th period as an example for illustration, the calculation formula for the corrected degree of abnormality in the t-th period can be expressed as:

[0098] ;

[0099] where represents the corrected degree of abnormality in the t-th period, represents the degree of trend abnormality in the t-th period, represents the degree of residual abnormality in the t-th period, represents the residual anomaly degree of the r-th cycle other than the t-th cycle, N represents the total number of cycles, and exp( ) represents the exponential function with the natural constant e as the base.

[0100] It reflects the difference between the residual anomaly degree of the t-th cycle and that of other cycles. The larger its value, the more discrete the residual anomaly degree of the t-th cycle is, the greater the difference from other cycles, indicating that the possibility of noise data existing in the operation data within the t-th cycle is greater. Therefore, it is necessary to adjust the trend anomaly degree to obtain a greater anomaly degree.

[0101] The adjusted anomaly degree It reflects the size of the actual noise anomaly after the cycle is adjusted by the residual anomaly degree. The larger its value, the greater the proportion of noise anomaly in the data anomaly degree within the t-th cycle, and the smaller the proportion of the actual anomaly situation of the corresponding operation data. Therefore, the exponential function is used for negative correlation mapping to obtain the corrected anomaly degree.

[0102] The corrected anomaly degree of each cycle characterizes the actual abnormal operation situation of the operation data within each cycle that is not affected by other external factors. Furthermore, by comparing the difference between the corrected anomaly degree and the trend anomaly degree, it can be understood whether there is noise in the data within each cycle. Specifically, the weight adjustment degree of the cycle is obtained according to the absolute value of the difference between the corrected anomaly degree and the trend anomaly degree, and there is a positive correlation between the absolute value of the difference and the weight adjustment degree.

[0103] In this embodiment, the calculation formula for the weight adjustment degree of the t-th cycle can be expressed as:

[0104]

[0105] where represents the weight adjustment degree of the t-th cycle, represents the corrected anomaly degree of the t-th cycle, represents the trend anomaly degree of the t-th cycle. represents the difference between the corrected anomaly degree and the trend anomaly degree of the t-th cycle, reflecting the difference between the actual anomaly degree of the t-th cycle not affected by other factors and the comprehensive anomaly degree. The smaller this difference, the closer the anomaly degree of the change trend of the operation data within this cycle is before and after excluding the interference of external factors, indicating that the abnormal situation within this cycle is a real abnormal phenomenon, and the value of the corresponding weight adjustment degree is smaller, that is, it is not necessary to perform a large degree of adjustment operation on the initial time series weight of this cycle.

[0106] The larger the value of [], it indicates that there is a relatively large difference in the degree of abnormality between the change trends of the operation data within this period before and after excluding external factor interferences. It shows that although there are certain abnormal conditions in the operation data of the t-th period, these abnormal conditions may be abnormal conditions caused by external environmental factors. Therefore, the reference value of using the operation data within this period for predictive analysis is relatively low, and it is necessary to adjust the weight corresponding to the operation data within this period to a smaller value. That is, the larger the value of the weight adjustment degree, the greater the need to perform a larger adjustment operation on the initial time series weight of this period.

[0107] Finally, use the weight adjustment degree to adjust the initial time series weight of each period, that is, perform a normalization process on the difference between the initial time series weight of the period and the weight adjustment degree to obtain the preferred time series weight of this period.

[0108] For the initial time series weight of each period, perform a reduction operation based on the weight adjustment degree on the basis of the initial time series weight, so that the weight corresponding to the period with noise interference is relatively low, and the weight corresponding to the period without noise interference is relatively high. Finally, use the preferred time series weight of each period to perform predictive analysis on the operation data within each period to obtain the prediction result of the operation data. In this embodiment, a weighted moving average prediction algorithm is used to perform predictive analysis on the operation data within each period. This algorithm is a well-known technology and will not be introduced in detail here. As Figure 6 shown in the prediction result of the operation data, since the time length of the horizontal axis from 0 to 1 is the time length of the set time period, the horizontal axis starts from 1 for the prediction data.

[0109] Furthermore, monitor the water conservancy construction drilling process based on the prediction result. In this embodiment, by setting a vibration threshold, when the operation data in the prediction result is greater than or equal to the preset vibration threshold, it indicates that an abnormal situation is about to occur during the drilling process, and relevant staff can be reminded to check through an early warning. Among them, the vibration threshold can be the average value of all vibration frequencies of the drilling equipment during a certain period of normal operation, or the standard vibration frequency set by relevant staff according to work experience for each different drilling operation.

[0110] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for evaluating the drilling quality in bridge construction, characterized in that, The method includes the following steps: Obtain the operation data during the water conservancy construction drilling process within a set time period before the current moment; Perform STL decomposition on the operation data to obtain the trend item data and residual item data for each period; according to the time interval between each period and the current moment, obtain the initial time series weight for each period; Based on the difference between the distribution of the trend item data for each period and the distribution of the trend item data for the adjacent period, the degree of dispersion of the trend item data for each period, and the difference between the trend item data for each period and the adjacent period, obtain the degree of trend anomaly for each period; Based on the degree of dispersion of the residual item data for each period and the difference between the degree of dispersion of the residual item data for each period and that of other periods, obtain the degree of residual anomaly for each period; Based on the degree of trend anomaly, the degree of residual anomaly, and the initial time series weight, obtain the optimal time series weight for each period, use the optimal time series weight to predict the operation data, and monitor the water conservancy construction drilling process based on the prediction result; The obtaining of the optimal time series weight for each period based on the degree of trend anomaly, the degree of residual anomaly, and the initial time series weight specifically includes: For any period, use the degree of residual anomaly for this period to correct the degree of trend anomaly to obtain the corrected degree of anomaly for this period; according to the absolute value of the difference between the corrected degree of anomaly and the degree of trend anomaly, obtain the degree of weight adjustment for this period, and there is a positive correlation between the absolute value of the difference and the degree of weight adjustment; Normalize the difference between the initial time series weight for this period and the degree of weight adjustment to obtain the optimal time series weight for this period; The obtaining of the initial time series weight for each period according to the time interval between each period and the current moment specifically includes: For any period, obtain the shortest time interval between this period and the current moment, and perform positive correlation normalization on the shortest time interval to obtain the initial time series weight for this period.

2. The method for evaluating the drilling quality for bridge construction according to claim 1, wherein The obtaining of the degree of trend anomaly for each period based on the difference between the distribution of the trend item data for each period and the distribution of the trend item data for the adjacent period, the degree of dispersion of the trend item data for each period, and the difference between the trend item data for each period and the adjacent period specifically includes: Denote any period as the target period, and according to the difference between each trend item data for the target period and the adjacent trend item data, obtain the data fluctuation degree of each trend item data for the target period, and calculate the variance of the data fluctuation degrees of all trend item data for the target period to obtain the first characteristic coefficient; Calculate the difference between the mean and the median of all trend item data for the target period as the trend distribution degree of the target period; Based on the difference between the trend distribution degrees of the target period and the adjacent period, and the difference between the mean values of the trend item data between the target period and other periods, obtain the second characteristic coefficient; The degree of trend anomaly of the target period is obtained based on the first characteristic coefficient and the second characteristic coefficient, and both the first characteristic coefficient and the second characteristic coefficient are positively correlated with the degree of trend anomaly.

3. A method for evaluating the drilling quality for bridge construction according to claim 2, characterized in that, The specific calculation formula of the second characteristic coefficient is: ； Among them, represents the second characteristic coefficient of the target period, and \(t\) represents the \(t\)-th period, represents the degree of trend distribution of the \(t\)-th period, represents the degree of trend distribution of the \((t - 1)\)-th period, represents the degree of the \((t + 1)\)-th trend distribution, and \(N\) represents the total number of periods, represents the mean value of all trend item data within the \(t\)-th period, represents the mean value of all trend item data within the \(r\)-th period other than the \(t\)-th period.

4. The method for evaluating the drilling quality for bridge construction according to claim 2, wherein The specific calculation formula of the data fluctuation degree is: ； Among them, represents the data fluctuation degree of the i-th trend item data in the t-th cycle, represents the i-th trend item data in the t-th cycle, represents the (i - 1)-th trend item data in the t-th cycle, represents the (i + 1)-th trend item data in the t-th cycle.

5. A method for evaluating the drilling quality for bridge construction according to claim 1, characterized in that, Using the degree of residual anomaly in this period to correct the degree of trend anomaly to obtain the corrected anomaly degree of this period specifically includes: ； Among them, represents the degree of corrected anomaly in the t-th cycle, represents the degree of trend anomaly in the t-th cycle, represents the degree of residual anomaly in the t-th cycle, represents the degree of residual anomaly in the r-th cycle other than the t-th cycle, N represents the total number of cycles, and exp( ) represents the exponential function with the natural constant e as the base.

6. A method for evaluating the drilling quality for bridge construction according to claim 1, characterized in that, Obtaining the degree of residual anomaly of each period according to the dispersion degree of the residual term data in each period and the difference between the dispersion degree of the residual term data in each period and that in other periods specifically includes: Denote any period as the selected period, and obtain the residual data characteristics of the selected period according to the number of residual term data included in the selected period, the mean value of all residual term data, and the difference between each residual term data and the mean value of all residual term data; Obtain the degree of residual anomaly of the selected period according to the balance degree of the difference between the residual data characteristics of the selected period and those of each other period.

7. A method for evaluating the drilling quality for bridge construction according to claim 6, characterized in that, Obtaining the degree of residual anomaly of the selected period according to the balance degree of the difference between the residual data characteristics of the selected period and those of each other period specifically includes: Calculate the ratio between the residual data characteristics of the selected period and any other period, and take the absolute value of the difference between 1 and the ratio as the difference characteristic between the selected period and the any other period; calculate the mean value of the difference characteristics between the selected period and each other period to obtain the degree of residual anomaly of the selected period.

8. A method for evaluating the drilling quality for bridge construction according to claim 7, characterized in that, The specific calculation formula of the residual data characteristics of the selected period is: ； Among them, represents the residual data feature of the selected period, s represents the s-th period, represents the total number of residual term data included in the s-th period, represents the mean value of all residual term data in the s-th period, represents the u-th residual term data in the s-th period.

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