Reconnaissance data archiving and backup method and system for geotechnical engineering and medium

Abstract

The invention relates to the technical field of data processing, in particular to a geotechnical engineering-oriented investigation data archiving and backup method and system and a medium. According to the technical scheme, multiple types of investigation data of geotechnical engineering are obtained, and each type of investigation data corresponds to a single investigation category of geotechnical engineering; obtaining a plurality of target time periods of the plurality of types of investigation data according to the fluctuation characteristics of the data value of each type of investigation data along with the change of the acquisition time; as the target time period is the time period with the maximum data anomaly degree represented by each type of investigation data, performing anomaly reason analysis on the investigation category corresponding to each target time period to obtain the anomaly importance degree of each target time period; according to the abnormal importance degree of each target time period, configuring a threshold value for implementing data compression in the corresponding target time period; and according to the threshold value of each target time period, correspondingly compressing the investigation data of the target time period, and performing archiving and backup, thereby realizing accurate compression of the investigation data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a method, a system and a medium for archiving and backing up survey data for geotechnical engineering. Background Art

[0002] Geotechnical engineering faces various risks, such as geological disaster risks, foundation settlement risks, etc. Survey data can be used for risk assessment of geotechnical engineering. For example, in the construction planning of large public transportation facilities and urban roads, a large number of geological pre-surveys are often required. According to the pre-defined borehole distribution map, each borehole of the geotechnical engineering is surveyed and recorded one by one, so as to realize the recording and storage of survey data.

[0003] The data involved in geotechnical engineering surveys includes geological profiles, soil samples, groundwater levels, seismic wave velocities, etc. If these data are stored and transmitted in their original form, they will occupy huge storage space and bandwidth resources. In the practical application of geotechnical engineering, there is a certain tolerance for data accuracy. As long as the compressed data can still meet the requirements of engineering analysis, a certain degree of information loss can be accepted. Using lossy compression can reduce the storage and transmission costs of data, which is particularly important for large-scale engineering projects and can help reduce the overall budget. For the existing rotating door lossy compression algorithm, when the data compression threshold is not selected appropriately, the compression efficiency will be reduced, or the information loss of the compression exceeds the set tolerance. Summary of the Invention

[0004] In order to solve the technical problem of how to achieve accurate compression of survey data, the purpose of the present invention is to provide a method, a system and a medium for archiving and backing up survey data for geotechnical engineering. The specific technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides a method for archiving and backing up survey data for geotechnical engineering, the method comprising: Obtaining multiple types of survey data of geotechnical engineering, wherein each type of survey data corresponds to a single survey category of geotechnical engineering; According to the fluctuation characteristics of the data values of each type of survey data changing with the acquisition time, obtaining multiple target time periods of the multiple types of survey data, wherein the target time period is the time period when the data abnormality degree represented by each type of survey data is the largest; Performing set partitioning and intersection processing on the survey categories corresponding to each target time period in sequence, and determining whether each target time period is a single-type abnormal source or a diversity abnormal source according to the processing results; obtaining the abnormal factor of each target time period according to the overlapping abnormal duration represented by the single-type abnormal source or the diversity abnormal source; obtaining the abnormal importance degree of each target time period according to the abnormal factor of each target time period; Configure the threshold value for implementing data compression for the corresponding target period according to the abnormal importance of each target period; Compress the survey data for the target period accordingly based on the threshold value for each target period and perform archiving and backup.

[0005] In an alternative embodiment, multiple target periods for multiple types of survey data are obtained according to the fluctuation characteristics of the data values of each type of survey data changing with the acquisition time, including: If the abnormal fluctuation degree of the data value of each type of survey data presented in the preset acquisition cycle is greater than the first threshold, determine the acquisition time period of the corresponding survey data as the abnormal period; If there is survey data of multiple survey categories in the same abnormal period, determine the multiple survey categories in the same abnormal period as the abnormal category set; Obtain the correlation degree of the corresponding two survey categories according to the Pearson correlation coefficient, time overlap length, and abnormal period difference of the two survey categories in the abnormal category set; Perform comprehensive abnormal analysis and clustering processing based on all the correlation degrees, and determine the time period corresponding to the clustering result as the target period.

[0006] In an alternative embodiment, obtaining the correlation degree of the corresponding two survey categories according to the Pearson correlation coefficient, time overlap length, and abnormal period difference of the two survey categories in the abnormal category set includes: According to the formula , obtain the correlation degree of any two survey categories , where is the Pearson correlation coefficient of survey category w and survey category x at time t, is the abnormal period difference between survey category w and survey category x at time t, is the time overlap length between survey category w and survey category x at time t, is the normalization function.

[0007] In an alternative embodiment, performing comprehensive abnormal analysis and clustering processing based on all the correlation degrees, and determining the time period corresponding to the clustering result as the target period includes: When the correlation degree is greater than the second threshold, determine the corresponding abnormal period as the reference period; According to the formula , obtain the comprehensive abnormal degree at time t , where is the number of the first abnormal categories in the abnormal period at time t, W1 is the number of the second abnormal categories in the abnormal category set at time t, is the abnormal degree of survey category w at time t, is the average degree of association between the survey category w at time t and other survey categories in the set of abnormal categories. is the number of reference periods at time t; Perform clustering processing based on the absolute value of the difference between any two comprehensive abnormal degrees among all comprehensive abnormal degrees, and determine the time period corresponding to the clustering result as the target time period.

[0008] In an optional embodiment, analyze the abnormal reasons for the survey categories corresponding to each target time period to obtain the abnormal importance of each target time period, including: Perform set partitioning and intersection processing on the survey categories corresponding to each target time period in sequence, and determine whether each target time period is a single-category abnormal source or a diversity abnormal source according to the processing results; Obtain the abnormal factor of each target time period according to the overlapping abnormal duration represented by the single-category abnormal source or the diversity abnormal source; Obtain the abnormal importance of each target time period according to the abnormal factor of each target time period.

[0009] In an optional embodiment, perform set partitioning and intersection processing on the survey categories corresponding to each target time period in sequence, and determine whether each target time period is a single-category abnormal source or a diversity abnormal source according to the processing results, including: Partition the survey categories at each abnormal moment in each target time period into individual sets to obtain the set of abnormal categories at each abnormal moment in each target time period; Perform intersection processing on all sets of abnormal categories within the preset duration in each target time period to obtain the processing result of each target time period; If the overlapping abnormal duration represented by the processing result is greater than the third threshold, determine that the target time period is a single-category abnormal source; If the overlapping abnormal duration represented by the processing result is less than or equal to the third threshold, determine that the target time period is a diversity abnormal source.

[0010] In an optional embodiment, obtain the abnormal factor of each target time period according to the overlapping abnormal duration represented by the single-category abnormal source or the diversity abnormal source, including: According to the formula , obtain the abnormal factor of the e-th target time period , where is the maximum abnormal degree of all abnormal moments in the e-th target time period, K is the number of overlapping abnormal durations in the e-th target time period, is the overlapping abnormal duration represented by the single-category abnormal source or the diversity abnormal source, is the number of survey categories in the set of abnormal categories of the k-th overlapping abnormal duration in the e-th target time period. represents the number of all survey categories for the k-th overlapping abnormal duration in the e-th target period, where both e and k are natural numbers greater than 0.

[0011] In an alternative embodiment, according to the abnormal factors of each target period, the abnormal importance of each target period is obtained, including: According to the formula , the abnormal importance of the e-th target period is obtained , where is the abnormal factor of the e-th target period, Q is the number of target periods after the e-th target period, is the time interval between the e-th target period and the q-th target period, is the number of difference categories between the e-th target period and the q-th target period, and e is a natural number greater than 0.

[0012] In a second aspect, an exploration data archiving and backup system for geotechnical engineering provided by an embodiment of the present invention further includes: A collection terminal for collecting various types of exploration data of geotechnical engineering and outputting them externally, where each type of exploration data corresponds to a single exploration category of geotechnical engineering; A processing terminal connected to the collection terminal; the processing terminal is used to obtain multiple target periods of various types of exploration data according to the fluctuation characteristics of the data values of each type of exploration data changing with the collection time, where the target period is the period with the largest degree of data abnormality represented by each type of exploration data; The processing terminal is further used to perform set partitioning and intersection processing on the exploration categories corresponding to each target period in sequence, and determine whether each target period is a single-category abnormal source or a diversity abnormal source according to the processing result; obtain the abnormal factor of each target period according to the overlapping abnormal duration represented by the single-category abnormal source or the diversity abnormal source; obtain the abnormal importance of each target period according to the abnormal factor of each target period; configure the threshold for implementing data compression for the corresponding target period according to the abnormal importance of each target period; and compress the exploration data of the target period correspondingly according to the threshold of each target period and perform archiving and backup.

[0013] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of any method in the first aspect are implemented.

[0014] The present invention has the following beneficial effects: The technical solution of the present invention obtains various types of exploration data for geotechnical engineering, and each type of exploration data corresponds to a single exploration category of geotechnical engineering; according to the fluctuation characteristics of the data value of each type of exploration data changing with the acquisition time, multiple target time periods of various types of exploration data are obtained; since the target time period is the time period when the data abnormality degree represented by each type of exploration data is the largest, the abnormal cause analysis is carried out on the exploration category corresponding to each target time period to obtain the abnormal importance degree of each target time period; according to the abnormal importance degree of each target time period, the threshold value for implementing data compression is configured corresponding to the target time period; according to the threshold value of each target time period, the exploration data of the corresponding target time period is compressed and archived and backed up. This technical solution analyzes the target time period corresponding to the abnormal data from the complicated exploration data of geotechnical engineering, and configures the threshold value for implementing data compression based on the abnormal importance degree of the target time period, which can improve the compression accuracy of this type of data, retain more original data points, and thus realize the accurate compression of exploration data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of a method for archiving and backing up exploration data for geotechnical engineering provided by an embodiment of the present invention; Figure 2 It is a schematic structural diagram of a system for archiving and backing up exploration data for geotechnical engineering provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the drawings and preferred embodiments to elaborate in detail on a method, system and medium for archiving and backing up exploration data for geotechnical engineering proposed according to the present invention, its specific implementation manner, structure, characteristics 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.

[0018] 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.

[0019] The following specifically describes the specific solutions of a method, system, and medium for archiving and backing up exploration data for geotechnical engineering in conjunction with the accompanying drawings.

[0020] During the formation of rock and soil masses, various geological processes have occurred, and they are affected by multiple factors. They are a heterogeneous medium with the characteristic of spatial variability. Due to the diversity, complexity, and uncertainty of this rock and soil medium, it will have relatively adverse effects on engineering construction. Therefore, it is necessary to analyze the actual distribution law of geotechnical engineering based on exploration data. Currently, for the compression processing of exploration data, it is difficult to set an accurate data compression threshold, resulting in the exploration data of geotechnical engineering occupying a large amount of storage space and bandwidth resources during the archiving and backup process. The following embodiments of the present invention will specifically elaborate on how to solve the above problems.

[0021] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for archiving and backing up exploration data for geotechnical engineering provided by an embodiment of the present invention. This method can be applied to a processing terminal for exploration data to implement data archiving and backup. The processing terminal can be a computer or a server, as long as it can implement data processing. The method includes: S11. Obtain multiple types of exploration data for geotechnical engineering, where each type of exploration data corresponds to a single exploration category of geotechnical engineering.

[0022] Specifically, exploration data can be obtained based on the on-site exploration tasks of geotechnical engineering. For example, by drilling holes at predetermined exploration locations with a drill rig, obtaining rock and soil samples from different depths, and collecting multi-dimensional data in the rock and soil samples. The exploration data of each dimension is the exploration data of an exploration category. The multiple types of exploration data specifically include groundwater level monitoring data, soil moisture data, slope displacement, soil stress data, and surface settlement data, etc. Among them, the multiple types of exploration data can be processed by the method of decimal scaling standardization to make them have similar scales and distributions for better comparison. Decimal scaling standardization is a well-known technology, and the specific method will not be introduced here.

[0023] S12. According to the fluctuation characteristics of the data values of each type of exploration data changing with the acquisition time, obtain multiple target time periods for the multiple types of exploration data, where the target time period is the time period when the data abnormality degree represented by each type of exploration data is the largest.

[0024] Specifically, many phenomena in geotechnical engineering, such as soil settlement and groundwater changes, usually change relatively slowly. If the exploration data suddenly shows a large change, that is, the absolute value of the difference is large, it may mean that the soil or other engineering parameters have mutated, which is a warning signal for the safety of geotechnical engineering. Therefore, this situation needs to be focused on.

[0025] The fluctuation characteristic analysis of data can be implemented based on each type of exploration data. For example, calculate the absolute value of the difference between the th data and the th data in the time series of the exploration data of the w-th category, and perform normalization processing on it. Mark the absolute value of the difference greater than 0.6 as an abnormal moment, and mark consecutive adjacent abnormal moments as a period, that is, the target period.

[0026] Exemplarily, step S12 includes sub-steps S12-1 to S12-4, which are specifically described as follows: S12-1. If the abnormal fluctuation degree presented by the data value of each type of exploration data in the preset acquisition period is greater than the first threshold, determine the acquisition time period of the corresponding exploration data as the abnormal period. The preset acquisition period and the first threshold can be set based on actual needs, as long as the abnormal data can be accurately screened out. When the data value of the exploration data fluctuates in the preset acquisition period, it indicates that there may be a risk of geological change, and the exploration data collected under such changes needs to be focused on. Then determine the acquisition time period of the corresponding exploration data as the abnormal period.

[0027] When calculating the abnormal fluctuation degree of each type of exploration data, the formula can be used to obtain the abnormal fluctuation degree presented by the exploration category w in the preset acquisition period s; where represents the duration of the preset acquisition period s of the exploration category w; represents the maximum abnormal value of all abnormal moments of the exploration category w in the preset acquisition period s. The larger the maximum value, the greater the possibility of abnormality in this period; represents the range of the exploration category w in the preset acquisition period s. The larger the range, the greater the fluctuation amplitude of the exploration data of this category, and the more likely it is to be abnormal. Based on the above formula, the abnormal fluctuation degree of each type of exploration data can be accurately calculated, and then the abnormal period can be determined. The abnormal period represents the acquisition period corresponding to the data with large fluctuation characteristics in the obtained exploration data.

[0028] It can be understood that in the multi-category (or dimension) exploration data of geotechnical engineering, the data of each category has its own characteristics and normal range of changes, and abnormal situations may only occur in one or several specific categories. Analyzing the abnormal degree of single-category data first can quickly locate the abnormal period, and then combining the abnormal periods of the time series data of multiple categories to determine the target period with the most analysis value.

[0029] S12-2. If there are survey data of multiple survey categories during the same abnormal period, it indicates that the geotechnical engineering may be affected by multiple dimensions. It is necessary to focus on the survey data during this period. For example, heavy rainfall will cause the groundwater level to rise. After the groundwater level rises, it will lead to an increase in the water content of the rock and soil mass, thus causing the soil moisture data to rise. At the same time, the increase in the water content of the rock and soil mass may change its mechanical properties, resulting in changes in the strain data representing the stress of the rock and soil mass. If the rock and soil mass is located on a slope, the increase in water content may also lead to a decrease in the shear strength of the slope soil, thereby causing changes in slope displacement. Paying attention to the survey data of multiple survey categories during the same abnormal period is of great significance. Then, multiple survey categories during the same abnormal period are determined as the abnormal category set.

[0030] S12-3. According to the Pearson correlation coefficient, time overlap length, and abnormal period difference between two survey categories in the abnormal category set, the correlation degree between the corresponding two survey categories is obtained. Through the correlation degree, it can be determined whether there is a strong correlation between two survey categories in the abnormal category set. It can be understood that the stronger the correlation between the survey data of multiple categories during the abnormal period, the more it indicates that a certain problem causes the simultaneous abnormality of multiple categories of data in geotechnical engineering, resulting in a greater impact, and the more it needs to be analyzed keyly. If the correlation between the survey data of multiple categories during the abnormal period is small, it indicates that a certain problem only causes the abnormality of single-category survey data, resulting in a relatively small impact.

[0031] It should be noted that for any two abnormal survey categories at time t, the interpolation algorithm can be used to make the time periods where the t-th moments of the two categories are equal in length, that is, the shorter time period is interpolated and increased in length, and the Pearson correlation coefficient of the two equal-length time periods is calculated. . The time overlap length is the overlapping time length between two abnormal categories during the same abnormal period, and the abnormal period difference is the absolute value of the difference in the time lengths between two abnormal categories.

[0032] Exemplarily, the correlation degree between two survey categories is calculated according to the formula . is the correlation degree between any two survey categories, and is the correlation degree between survey category w and survey category x at time t. Among them, is the Pearson correlation coefficient between survey category w and survey category x at time t, is the abnormal period difference between survey category w and survey category x at time t, is the time overlap length between survey category w and survey category x at time t, is a normalization function. t is a natural number greater than 0, and w and x are natural numbers greater than 0 and not equal. It should be noted that to ensure the meaningfulness of the calculation results, in the fractional operations in the embodiments of the present invention, when the denominator is 0, a tuning factor greater than 0 needs to be added to the denominator to prevent the denominator from being 0. The value of the tuning factor is set by the implementer according to the actual situation, and no special restrictions are made in this application.

[0033] As can be seen from the above formula, the Pearson correlation coefficient The larger it is, the stronger the correlation between the data in the two time periods, and the higher the degree of abnormality at time t. In the subsequent compression process, the exploration data of multiple categories corresponding to this moment should be retained; The greater the time difference characterized, the more data supplemented by the interpolation algorithm, and the less reliable the data; the time overlap length characterizes the intersection of two time periods. When there is no intersection, let be 1.

[0034] S12-4. Perform comprehensive anomaly analysis and clustering processing according to all association degrees, and determine the time period corresponding to the clustering result as the target time period. By performing comprehensive anomaly analysis of all association degrees, the association relationship between various categories can be obtained more accurately, and then clustering processing can be performed to achieve category division to obtain the target time period.

[0035] Exemplarily, step S12-4 includes: The first step is to determine the abnormal time period corresponding to it as the reference time period when the association degree is greater than the second threshold. The second threshold can be set based on actual needs, for example, set to 0.7. In the above manner, the correlation between the exploration category w and the exploration category x at time t, that is, the association degree, is obtained. The association degrees greater than 0.7 are recorded as the reference time periods of the exploration category w at time t. The reference time period represents the time period with more important reference value for the abnormal exploration category w. For example, in rock mass engineering, the abnormal conditions caused by landslides in multiple dimensions are correlated. In order to obtain the time periods when the exploration data of multiple categories are all abnormal, it is necessary to analyze which time periods have a large correlation with each category, that is, the reference time periods of each time period. The more reference time periods corresponding to each time period under each category at the same moment, the more likely a landslide occurs at this moment.

[0036] The second step is to obtain the comprehensive anomaly degree at time t according to the formula . Among them, is the number of the first abnormal categories in the abnormal time period at time t. The larger the number, the greater the weight of this moment; W1 is the number of the second abnormal categories in the abnormal category set at time t; is the abnormal degree of the exploration category w at time t. The greater the abnormal degree, the more abnormal the exploration data of this category within the time period; is the average degree of association between the survey category w at time t and other survey categories in the abnormal category set. The larger the average value, the more similar the survey data of multiple categories are; is the number of reference time periods at time t. The larger this number, the more similar other categories in the abnormal category set are to the survey category w.

[0037] In the third step, clustering processing is performed using the absolute value of the difference between any two comprehensive abnormal degrees among all comprehensive abnormal degrees as the clustering distance, and the time period corresponding to the clustering result is determined as the target time period. Clustering processing uses the Kmeans clustering algorithm to cluster the absolute values of the differences, and several clusters can be obtained. Each cluster represents relatively close absolute values of the differences. After clustering, the absolute values of the differences grouped into one cluster are obtained, and the time period corresponding to each abnormal moment included in each cluster is the target time period.

[0038] S13. Analyze the abnormal causes of the survey categories corresponding to each target time period to obtain the abnormal importance of each target time period.

[0039] Specifically, based on the comprehensive abnormal degree at each moment, multiple target time periods are divided, and the comprehensive abnormal degrees at all moments within each target time period are similar. The abnormality of geotechnical engineering survey data may be caused by various factors, such as human operation errors, natural condition changes, etc. In the geotechnical engineering survey data, if the abnormal data categories are stable, this may mean that the abnormality originates from a fixed source, and the characteristics and patterns of the abnormal data are relatively easy to identify and track. If the abnormal data categories are constantly changing, it means that the causes of the abnormality are diverse, and at this time, more attention needs to be paid.

[0040] By analyzing the abnormal causes of the survey categories in the target time period, it can be determined whether the survey data in the target time period needs to be analyzed key points, and it is quantified through the abnormal importance. When implementing the abnormal cause analysis, a convolutional neural network model can be constructed and trained. After the training is completed, the survey categories and corresponding survey data in the target time period are input into the convolutional neural network model, and the abnormal importance of the target time period is obtained based on the output result of the model.

[0041] Exemplarily, step S13 includes sub-steps S13-1 to S13-3, which are specifically described as follows: S13-1. Perform set partitioning and intersection processing on the survey categories corresponding to each target time period in sequence, and determine whether each target time period is a single-category abnormal source or a diverse abnormal source according to the processing results. Set partitioning can be performed on each moment of each target time period, and intersection processing is performed on the partitioned sets. Through the intersection processing, it can be determined whether the abnormal moment persists in the partitioned sets, and then analyze whether the target time period is a single-category abnormal source or a diverse abnormal source.

[0042] Sub-step S13-1 specifically includes: In the first step, classify the survey categories of each abnormal moment in each target time period into a single set to obtain the abnormal category set of each abnormal moment in each target time period. Taking the first 5 moments of the e-th target time period as an example, is the category set of the 1st moment in the e-th target time period, is the category set of the 2nd moment in the e-th target time period, is the category set of the 3rd moment in the e-th target time period, is the category set of the 4th moment in the e-th target time period, is the category set of the 5th moment in the e-th target time period.

[0043] In the second step, perform an intersection operation on all abnormal category sets of the preset duration in each target time period to obtain the processing result of each target time period. Still taking the first 5 moments of the e-th target time period as an example, the intersection of all category sets is {B, C}, and is denoted as the target set of the intersection operation, that is, the processing result of target time period e. The duration when both survey categories B and C exist is the overlapping abnormal duration.

[0044] In the third step, if the overlapping abnormal duration represented by the processing result is greater than the third threshold, it indicates that the survey categories with abnormal data are stable, and the target time period is determined as the single-category abnormal source. If the overlapping abnormal duration represented by the processing result is less than or equal to the third threshold, it indicates that the survey categories with abnormal data are constantly changing, and the target time period is determined as the diversity abnormal source.

[0045] S13-2. Obtain the abnormal factor of each target time period according to the overlapping abnormal duration represented by the single-category abnormal source or the diversity abnormal source. The reason for the abnormal survey data can be obtained through the overlapping abnormal duration. Based on the abnormal reason, the abnormal factor is determined. A correspondence table can be constructed based on the correspondence relationship between the overlapping abnormal duration and the abnormal factor. After determining the current overlapping abnormal duration, the abnormal factor is determined in the correspondence table.

[0046] The abnormal factor can also be obtained according to the formula to obtain the abnormal factor of the e-th target time period . Among them, is the maximum abnormal degree of all abnormal moments under the e-th target time period, K is the number of overlapping abnormal durations under the e-th target time period, is the overlapping abnormal duration represented by the single-category abnormal source or the diversity abnormal source, is the number of survey categories in the abnormal category set with the kth overlapping abnormal duration in the eth target period, It represents the number of all survey categories with the kth overlapping abnormal duration in the eth target period. It can be understood that The larger the value is, the more survey categories with the kth overlapping abnormal duration in the eth target period are, and the more factors are involved in the abnormal data. Taking landslide in geotechnical engineering as an example, The larger it is, the more dimensions are affected by the landslide and the greater the anomaly during that period.

[0047] S13-3, according to the abnormal factors of each target period, obtain the abnormal importance of each target period. The abnormal importance of each target period can be obtained based on the corresponding relationship between the abnormal factors and the abnormal importance. The abnormal importance can also be further normalized to meet the requirements of the subsequent configuration threshold value.

[0048] In geotechnical engineering survey data, due to the cumulative effect, anomalies in one dimension may accumulate over time and gradually affect other dimensions. For example, long-term groundwater level rise may be caused by natural disasters such as earthquakes and floods, which may first affect data in one dimension and then affect other dimensions through complex geological processes. Therefore, it is necessary to further analyze the abnormal change trend of geotechnical engineering survey data, which leads to increased soil saturation and then affects the mechanical properties of the soil. Therefore, based on the above method, the corresponding anomaly importance is determined by the abnormal factor of the target period, which may be insufficient in accuracy.

[0049] Based on this, in a specific implementation, according to the formula , get the abnormal importance of the e-th target period .in, is the abnormal factor of the e-th target period; Q is the number of target periods after the e-th target period; is the time interval between the e-th target period and the q-th target period; is the number of difference categories between the e-th target period and the q-th target period. The more difference categories there are, the more the survey category in the e-th target period gradually increases the impact of subsequent other category data. The smaller it is, the faster the impact is, and the more important the e-th period is. In the time series, the target period e is before the target period q, and there is at least one target period between the two.

[0050] S14. According to the abnormal importance of each target time period, a threshold value for implementing data compression in the corresponding target time period is configured.

[0051] Specifically, set the minimum threshold , the adaptive size of the threshold is . Take as the threshold for implementing data compression in the e-th target period. Similarly, the thresholds for all target periods are obtained.

[0052] S15. According to the threshold of each target period, compress the exploration data of the target period correspondingly and conduct archiving and backup.

[0053] Specifically, use the rotating door algorithm to compress the collected abnormal exploration data to reduce the storage capacity. The greater the importance of the target period, the smaller the corresponding threshold. At this time, the compression rate decreases, but the compression accuracy will increase, and more original data points will be retained. The smaller the importance of the target period, the larger the corresponding threshold, and more data points will be considered within the acceptable error range, and more data points will be compressed, resulting in an increase in the compression rate.

[0054] It should be noted that the above method is for the compression of abnormal data in exploration data. This type of data has great analysis value and more original data points need to be retained. For the compression of normal data in exploration data, a maximum threshold can be set to compress the periods of non-target periods (normal periods, that is, the possibility of landslides is very small).

[0055] The following will comprehensively elaborate on the exploration data archiving and backup method for geotechnical engineering, which is specifically elaborated as follows: S101. Obtain various types of exploration data of geotechnical engineering.

[0056] S102. Screen out several abnormal periods from the time series of exploration data of each category, obtain the target period based on the abnormal period with the maximum abnormal degree, and calculate the abnormal degree of the exploration data of each category in the abnormal period.

[0057] S103. Calculate the correlation between any two categories of abnormal periods in which the target period is located.

[0058] S104. Calculate the comprehensive abnormal degree of each moment in the target period.

[0059] S105. Screen out several target periods based on the comprehensive abnormal degree.

[0060] S106. Calculate the abnormal factor of each target period.

[0061] S107. Calculate the abnormal importance of each target period.

[0062] S108. Configure the threshold to implement the compression processing of abnormal data and conduct archiving and backup.

[0063] Based on the same technical concept as the backup method, an embodiment of the present invention further provides an exploration data archiving and backup system for geotechnical engineering. Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of the backup system. The backup system includes a collection terminal 1 and a processing terminal 2.

[0064] The collection terminal 1 is used to collect various types of exploration data of geotechnical engineering and output them externally. Among them, each type of exploration data corresponds to a single exploration category of geotechnical engineering. The collection terminal can be various sensors, etc., and obtains exploration data based on the exploration tasks of geotechnical engineering.

[0065] The processing terminal 2 is connected to the collection terminal 1; the processing terminal 2 is used to obtain multiple target time periods of various types of exploration data according to the fluctuation characteristics of the data value of each type of exploration data changing with the collection time. Among them, the target time period is the time period when the data abnormality degree represented by each type of exploration data is the largest; the processing terminal is also used to analyze the abnormal reasons for the exploration category corresponding to each target time period to obtain the abnormal importance degree of each target time period; configure the threshold value for implementing data compression for the corresponding target time period according to the abnormal importance degree of each target time period; and compress the exploration data of the target time period correspondingly according to the threshold value of each target time period and perform archiving and backup.

[0066] Based on the same technical concept as the backup method, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the backup methods are implemented.

[0067] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. A method for archiving and backing up survey data for geotechnical engineering, characterized in that: The method comprises: Acquire multiple types of survey data of geotechnical engineering, wherein each type of survey data corresponds to a single survey category of the geotechnical engineering; According to the fluctuation characteristics of the data value of each type of survey data changing with the acquisition time, a plurality of target time periods of the plurality of types of survey data are obtained, wherein the target time period is the time period in which the data abnormality represented by each type of survey data is the greatest; Perform set division and intersection processing on the survey categories corresponding to each target period in turn, and determine whether each target period is a single type of abnormal source or a diverse abnormal source according to the processing results; obtain the abnormal factor of each target period according to the overlapping abnormal duration represented by the single type of abnormal source or the diverse abnormal source; obtain the abnormal importance of each target period according to the abnormal factor of each target period; According to the abnormal importance of each target period, configure the threshold value for implementing data compression in the corresponding target period; According to the threshold value of each target period, the survey data of the target period is compressed and archived for backup.

2. The method for archiving and backing up survey data for geotechnical engineering according to claim 1, characterized in that: The step of obtaining a plurality of target time periods of the plurality of types of survey data according to the fluctuation characteristics of the data value of each type of survey data as the data is collected over time comprises: If the abnormal fluctuation degree of the data value of each type of survey data in the preset collection period is greater than a first threshold, determining the collection time period of the corresponding survey data as an abnormal period; If there are survey data of multiple survey categories in the same abnormal period, the multiple survey categories in the same abnormal period are determined as an abnormal category set; Obtaining the correlation degree of the two survey categories according to the Pearson correlation coefficient, time overlap length and abnormal time period difference of the two survey categories in the abnormal category set; Comprehensive anomaly analysis and clustering processing are performed according to all correlation degrees, and the time period corresponding to the clustering result is determined as the target time period.

3. The method for archiving and backing up survey data for geotechnical engineering according to claim 2, characterized in that: The obtaining of the correlation degree of the two survey categories according to the Pearson correlation coefficient, the time overlap length and the abnormal time period difference of the two survey categories in the abnormal category set includes: According to the formula , get the correlation degree between any two survey categories ,in, is the Pearson correlation coefficient between survey category w and survey category x at time t, is the difference between the abnormal time periods of survey category w and survey category x at time t, is the time overlap length between survey category w and survey category x at time t, is the normalization function.

4. The method for archiving and backing up survey data for geotechnical engineering according to claim 2, characterized in that: The comprehensive abnormality analysis and clustering process are performed according to all the correlation degrees, and the time period corresponding to the clustering result is determined as the target time period, including: When the correlation degree is greater than a second threshold, determining the abnormal time period corresponding thereto as a reference time period; According to the formula , get the comprehensive abnormality degree at time t ,in, is the number of first abnormal categories in the abnormal period at time t, W1 is the number of second abnormal categories in the abnormal category set at time t, is the abnormality degree of survey category w at time t, is the average value of the correlation between the survey category w and other survey categories in the abnormal category set at time t, is the number of reference periods at time t; Clustering processing is performed according to the absolute value of the difference between any two comprehensive abnormality levels among all comprehensive abnormality levels as the clustering distance, and the time period corresponding to the clustering result is determined as the target time period.

5. The method for archiving and backing up survey data for geotechnical engineering according to claim 1, characterized in that: The survey categories corresponding to each target period are sequentially divided into sets and intersection-finding processes are performed, and each target period is determined to be a single-type abnormal source or a diverse abnormal source according to the processing results, including: Divide the survey categories at each abnormal moment in each target period into a single set to obtain an abnormal category set at each abnormal moment in each target period; The intersection of all abnormal category sets of the preset duration in each target period is calculated to obtain the processing result of each target period; If the overlapping abnormality duration represented by the processing result is greater than a third threshold, determining that the target time period is a single type of abnormality source; If the overlapping abnormality duration represented by the processing result is less than or equal to a third threshold, the target time period is determined to be a diversity abnormality source.

6. The method for archiving and backing up survey data for geotechnical engineering according to claim 1, characterized in that: The obtaining of the abnormal factor of each target time period according to the overlapping abnormal duration represented by the single abnormal source or the diverse abnormal sources includes: According to the formula , get the abnormal factor of the e-th target period ,in, is the maximum abnormal degree of all abnormal moments in the e-th target period, K is the number of overlapping abnormal durations in the e-th target period, The duration of the overlapping anomalies represented by the single anomaly source or the multiple anomaly sources, is the number of survey categories in the abnormal category set with the kth overlapping abnormal duration in the eth target period, Represents the number of all survey categories with the kth overlapping abnormal duration in the eth target period.

7. The method for archiving and backing up survey data for geotechnical engineering according to claim 1, characterized in that: The step of obtaining the abnormal importance of each target period according to the abnormal factor of each target period includes: According to the formula , get the abnormal importance of the e-th target period ,in, is the abnormal factor of the e-th target period, Q is the number of target periods after the e-th target period, is the time interval between the e-th target period and the q-th target period, is the number of difference categories between the e-th target period and the q-th target period, where e is a natural number greater than 0.

8. A geotechnical engineering survey data archiving and backup system, characterized in that: The system comprises: A collection terminal is used to collect multiple types of survey data of geotechnical engineering and output them to the outside, wherein each type of survey data corresponds to a single survey category of the geotechnical engineering; A processing terminal connected to the acquisition terminal; the processing terminal is used to obtain multiple target time periods of the multiple types of survey data according to the fluctuation characteristics of the data value of each type of survey data changing with the acquisition time, wherein the target time period is the time period with the maximum degree of data anomaly represented by each type of survey data; The processing terminal is also used to perform set division and intersection processing on the survey categories corresponding to each target time period in turn, and determine whether each target time period is a single type of abnormal source or a variety of abnormal sources according to the processing results; obtain the abnormal factor of each target time period according to the overlapping abnormal duration represented by the single type of abnormal source or the variety of abnormal sources; obtain the abnormal importance of each target time period according to the abnormal factor of each target time period; configure the threshold value for implementing data compression for the corresponding target time period according to the abnormal importance of each target time period; and compress the survey data of the target time period according to the threshold value of each target time period and archive and back up it.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

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