Acquisition and testing method of soft rock mechanical parameters based on big data

Abstract

The present invention discloses a method for acquiring and testing soft rock mechanical parameters based on big data, and relates to the technical field of intelligent exploration system construction, including dividing the soft rock distribution area into rock layers, obtaining pressure parameter acquisition nodes and water absorption parameter acquisition nodes, obtaining a pressure test data group, and calculating a pressure change line graph; obtaining a water absorption test data group, and calculating a water absorption change line graph; matching a pressure test platform and a water absorption test platform based on zoning attributes, and determining the respective maximum values ​​of the paired pressure test platform and water absorption test platform and making judgments. The method calculates two test objects of each test platform separately, and then makes discrete comparisons according to the respective calculation results, and judges whether the test platform has a large error according to the discrete comparison results, and removes the data tested by the test platform in advance to reduce the errors of subsequent calculations, thereby ensuring the accuracy of the final analysis results.

Description

Technical Field

[0001] The present invention relates to the field of soft rock mechanics, and in particular to a method for obtaining and testing soft rock mechanics parameters based on big data. Background Art

[0002] The intelligent exploration system is generally composed of an intelligent rationality layer, a fusion network layer, a platform support layer, and an application service layer. The premise of intelligent management is that the rationality layer needs to obtain correct and appropriate soft rock data. The current soft rock data acquisition method is achieved through various test platforms. The test platform obtains first-hand test data through different types of data testing tools or instruments, and pre-processes the tested data and passes it to subsequent layers, thereby achieving the purpose of intelligent management.

[0003] At present, there is a problem of inaccurate data detection in the testing work in the urban soft rock distribution area. One of the reasons is that there are multiple test platforms arranged in the soft rock distribution area of ​​the same area. Each test platform will upload its own test data, such as soft rock compressive strength and soft rock water absorption data. The test data is generally pre-processed, that is, averaged, to minimize the error of all test data. However, the above processing process will have a large gap with the real data, especially most of the test platforms in this area have low test accuracy, which is not conducive to obtaining the final accurate results. Summary of the invention

[0004] The purpose of the present invention is to provide a method for acquiring and testing soft rock mechanical parameters based on big data. The method calculates two test objects on each test platform separately, and then performs discrete comparison based on their respective calculation results. According to the discrete comparison results, it is judged whether the test platform has a large error, and the data tested by the test platform is eliminated in advance to reduce the errors of subsequent calculations, thereby ensuring the accuracy of the final analysis results.

[0005] The embodiment of the present invention is achieved as follows:

[0006] In terms of pressure, the soft rock mechanical parameter acquisition and testing method based on big data includes the following steps: dividing the soft rock distribution area into rock layers, obtaining pressure parameter acquisition nodes and water absorption rate parameter acquisition nodes, wherein the pressure parameter acquisition node is used to test the compressive strength of soft rocks in the soft rock distribution area, and the water absorption rate parameter acquisition node is used to test the water absorption rate of soft rocks in the soft rock distribution area; obtaining all pressure test platforms in the pressure parameter acquisition node, and determining the test data of all pressure test platforms to form a pressure test data group, and calculating the maximum value of each test data in the pressure test data group to obtain a pressure change line graph; obtaining the water absorption rate parameter acquisition node All water absorption test platforms within the point, and determine the test data of all water absorption test platforms to form a water absorption test data group, and calculate the maximum value of each test data in the water absorption test data group to obtain a water absorption change line graph; match the pressure test platform and the water absorption test platform based on the zoning attributes, and determine the respective maximum values ​​of the paired pressure test platform and water absorption test platform and make a judgment; if the time node at which the maximum value of the pressure test platform in the pressure change line graph is located is consistent with the time node at which the maximum value of the water absorption test platform in the water absorption change line graph is located, then the pressure test platform and the water absorption test platform are used as data rationality test points.

[0007] In an optional implementation, a pressure test platform serving as a data rationality point is calibrated as a pressure target test platform, a pressure topology structure diagram is established based on all pressure test platforms, pressure-associated test points of the pressure target test platform are determined based on the pressure topology structure diagram, all pressure-associated test points are compared with the data tested by the pressure target test platform for similarity, and whether to use the pressure target test platform as a data rationality test point is determined based on the comparison result; and / or, a water absorption test platform serving as a data rationality point is calibrated as a water absorption target test platform, and a water absorption topology structure diagram is established based on all water absorption test platforms;

[0008] Based on the water absorption topological structure diagram, the water absorption associated test points of the water absorption target test platform are determined; all water absorption associated test points are compared with the data tested by the water absorption target test platform for similarity, and according to the comparison results, it is determined whether to use the water absorption target test platform as a data rationality test point.

[0009] In an optional implementation, the similarity comparison includes the following steps: determining a means of acquiring test data, wherein the acquisition means include chemical tests, physical tests, and biological tests; determining a proportion between each sub-item in the acquisition means, calculating a distance coefficient between the proportion of each sub-item in the corresponding associated test point acquisition means and the proportion of each sub-item in the corresponding target test platform acquisition means, obtaining a central tendency of all distance coefficients, and calculating a similarity comparison result between the corresponding associated test point and the corresponding target test platform based on the central tendency.

[0010] In an optional implementation, before obtaining the central tendency of all distance coefficients, the following steps are also included: all distance coefficients are arranged to obtain a pressure sequence; each sub-item in the pressure sequence is assigned a basic value, and then a correction value is assigned to each basic value based on the corresponding topological structure diagram to obtain multiple correction results, and the distance coefficients corresponding to the first P correction results are determined among the multiple correction results to perform subsequent steps, where P is a positive integer.

[0011] In an optional implementation manner, when determining P correction results, the correction result corresponding to the abnormal distance coefficient is supplemented, wherein the abnormal distance coefficient is obtained by fitting based on all distance coefficients.

[0012] In an optional embodiment, after determining the pressure-associated test points of the pressure target test platform based on the pressure topological structure diagram, the following steps are also included: obtaining relative exploration parameters between the pressure target test platform and the pressure-associated test points;

[0013] The pressure-related test points whose relative exploration parameters exceed the preset pressure values ​​are eliminated.

[0014] In an optional implementation, eliminating pressure-associated test points whose relative exploration parameters exceed the preset pressure value also includes the following steps: determining the number of nodes Q between the eliminated pressure-associated test points and the pressure target test platform based on the pressure topology structure diagram, and if the Q value is greater than or equal to the preset water absorption rate value, then adding the eliminated pressure-associated test points as the basis for comparison with the pressure target test platform.

[0015] In an optional implementation, a mechanical distribution coefficient is assigned to the supplemented pressure-related test point, and the mechanical distribution coefficient is used as a calculation basis for substituting the pressure-related test point for similarity comparison.

[0016] In terms of water absorption, the acquisition and testing methods of soft rock mechanical parameters based on big data include:

[0017] A pressure division module is used to divide the soft rock distribution area into rock layers, and obtain a pressure parameter acquisition node and a water absorption rate parameter acquisition node, wherein the pressure parameter acquisition node is used to test the compressive strength of soft rocks in the soft rock distribution area, and the water absorption rate parameter acquisition node is used to test the water absorption rate of soft rocks in the soft rock distribution area;

[0018] A pressure calculation module, which is used to obtain all pressure test platforms in the pressure parameter acquisition node, and determine the test data of all pressure test platforms to form a pressure test data group, and calculate the maximum value of each test data in the pressure test data group to obtain a pressure change line graph;

[0019] A water absorption rate calculation module, which is used to obtain all water absorption rate test platforms in the water absorption rate parameter acquisition node, and determine the test data of all water absorption rate test platforms to form a water absorption rate test data group, and calculate the maximum value of each test data in the water absorption rate test data group to obtain a water absorption rate change line graph;

[0020] A pressure matching module, which is used to match the pressure test platform and the water absorption test platform based on the zoning attributes, determine the maximum values ​​of the paired pressure test platform and the water absorption test platform, and make a judgment;

[0021] The pressure judgment module is used to judge: if the time node at which the maximum value of the pressure test platform in the pressure change line graph is located is consistent with the time node at which the maximum value of the water absorption rate test platform in the water absorption rate change line graph is located, then the pressure test platform and the water absorption rate test platform are used as data rationality test points.

[0022] In an optional embodiment, it also includes a water absorption rate judgment module, which is used to calibrate the pressure test platform as a data rationality point as a pressure target test platform, establish a pressure topology structure diagram based on all the pressure test platforms, determine the pressure-related test points of the pressure target test platform based on the pressure topology structure diagram, perform a similarity comparison between all the pressure-related test points and the data tested by the pressure target test platform, and judge whether to use the pressure target test platform as a data rationality test point according to the comparison result; and / or, calibrate the water absorption rate test platform as a data rationality point as a water absorption rate target test platform, establish a water absorption rate topology structure diagram based on all the water absorption rate test platforms; determine the water absorption rate-related test points of the water absorption rate target test platform based on the water absorption rate topology structure diagram; perform a similarity comparison between all the water absorption rate-related test points and the data tested by the water absorption rate target test platform, and judge whether to use the water absorption rate target test platform as a data rationality test point according to the comparison result.

[0023] The beneficial effects of the embodiments of the present invention are:

[0024] The soft rock mechanical parameter acquisition and testing method based on big data provided by the embodiment of the present invention divides the test platforms corresponding to various types of test data in the soft rock distribution area, such as the main soft rock compressive strength and soft rock water absorption data, to form pressure parameter acquisition nodes and water absorption parameter acquisition nodes, and performs longitudinal comparative analysis on each type of test data, especially the probability that only one of the soft rock compressive strength data and the soft rock water absorption data of the same test platform is abnormal is very low, either the same or similar degree of abnormality occurs, or it is a normal situation. By using the above longitudinal comparison method, the pressure test platform and the water absorption test platform at the corresponding matching zoning position are finally matched to determine whether the abnormality of the two is consistent or tends to be consistent to judge whether the data testing function of the test platform is normal, thereby pre-excluding the test platform that may have abnormal tests and not participating in the subsequent data analysis process, thereby ensuring the accuracy of the final calculation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 is a flow chart of the first step of the method of the present invention;

[0027] Figure 2 Flow chart of the second step of the method of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0029] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] It should be understood that the "system", "device" and / or "module" used in the present invention is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0031] As shown in the present invention and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular, but also include the plural. In general, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list, and the method or device may also include other steps or elements.

[0032] Flowcharts are used in the present invention to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, the various steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0033] Embodiment: In the testing operation of the urban soft rock distribution area, when conducting data testing for the soft rock distribution area in the same area, data transmitted by multiple test platforms will be obtained. In some cases, there will be test errors between these test data, that is, the test data of the same object on different test platforms are not exactly the same. The previous processing method is to average all the test data to minimize the error, but there are still situations where the accuracy can be further improved for this processing method, especially when most of the above test platforms have test accuracy problems. At this time, the average processing results are still far from the actual results and cannot provide reliable data support for the subsequent smart management application level. In order to further optimize the above problems, this embodiment provides a soft rock mechanical parameter acquisition and testing method based on big data, which further improves the accuracy of data testing by judging in advance that there are test platforms with low test accuracy among the test platforms for the same object and eliminating them.

[0034] like Figure 1 As shown, the method for obtaining and testing soft rock mechanical parameters based on big data provided in this embodiment includes the following steps:

[0035] S100: Divide the soft rock distribution area into rock layers, obtain pressure parameter acquisition nodes and water absorption rate parameter acquisition nodes, wherein the pressure parameter acquisition node is used to test the soft rock compressive strength in the soft rock distribution area, and the water absorption rate parameter acquisition node is used to test the soft rock water absorption rate in the soft rock distribution area; this step indicates determining the soft rock distribution area, which is mainly equipped with two test functions of soft rock water absorption rate and soft rock compressive strength, which are realized by multiple test platforms arranged in the soft rock distribution area, and each test platform will perform synchronous tests of soft rock water absorption rate and soft rock compressive strength at corresponding points. Considering that the probability of normal or abnormal soft rock water absorption rate test and soft rock compressive strength test on the same test platform is greater, and the probability of abnormality in only one of them is smaller, the test data of the two functions corresponding to each test platform are separately extracted for longitudinal comparison, which can further determine whether the test platform has the possibility of abnormal test. That is, the rock layers in the soft rock distribution area are divided into pressure parameter acquisition nodes and water absorption rate parameter acquisition nodes for subsequent longitudinal comparison.

[0036] S200: Obtain all the pressure test platforms within the pressure parameter acquisition node, and determine the test data of all the pressure test platforms to form a pressure test data group, and calculate the maximum value of each test data within the pressure test data group to obtain a pressure change line graph. This step represents the use of the maximum value of the test data to achieve the above-mentioned longitudinal comparison. Since this step is unclear whether there is a possibility of abnormal testing on the test platform, it is necessary to present and analyze the test data of all pressure test platforms. Similarly, the same processing measures are taken for the water absorption test platform, that is, step S300 is performed: obtain all the water absorption test platforms within the water absorption parameter acquisition node, and determine the test data of all the water absorption test platforms to form a water absorption test data group, and calculate the maximum value of each test data within the water absorption test data group to obtain a water absorption change line graph; present and analyze the test data of all water absorption test platforms.

[0037] In step S200 and step S300, by presenting the pressure change line graph and the water absorption rate change line graph, it is possible to find the comparison between the data of the two functional tests of the same test platform and the data of the two functional tests of the other test platforms. If the data change curves of the two functional tests of the same test platform are consistent, it means that the test data of the two functions are accurate or correct from the perspective of the test platform, and then step S400 can be performed: matching the pressure test platform and the water absorption rate test platform based on the zoning attributes, and determining the respective maximum values ​​of the paired pressure test platform and the water absorption rate test platform and making a judgment; the zoning attributes here mainly refer to the situation that they belong to the same configuration and division entity. In practice, the soft rock compressive strength and soft rock water absorption rate tests may be carried out at different test points, but the different test points are all under the supervision and responsibility of the same entity, and it is necessary to combine the corresponding separate pressure test platform and water absorption rate test platform to perform the above-mentioned longitudinal comparison, especially based on their own maximum values ​​and make a judgment.

[0038] S500: If the time node at which the maximum value of the pressure test platform in the pressure change line graph is located is consistent with the time node at which the maximum value of the water absorption test platform in the water absorption change line graph is located, then the pressure test platform and the water absorption test platform are used as data rationality test points; this step means that if the pressure test platform and the water absorption test platform matched as the same test point have the same or roughly the same change lines in their respective change line graphs, then it can be determined that they are preliminarily judged to be points that may belong to normal and accurate test data, and if their respective change lines are far apart, then they can be judged to be abnormal test points, and their uploaded data will be discarded to achieve the purpose of further reducing data errors.

[0039] Through the above technical solution, only the test data of the test points with high possibility of abnormality are eliminated from the perspective of preliminary screening, and the retained test points may also have abnormal test situations, but compared with the method of applying the test data of all test points as the average, the error is smaller and the data acquisition is more reliable. In some embodiments, in order to further analyze the retained test points, in order to further determine whether the test points are abnormal. Figure 2 As shown, the analysis method provided in this embodiment also includes step S600 and its sub-steps.

[0040] S600 is a step of finding associated test points by establishing a topological graph structure and performing similarity comparison to achieve further screening of the pressure test platform and the water absorption test platform, specifically: calibrating the pressure test platform as a data rationality point as a pressure target test platform, establishing a pressure topological structure graph based on all the pressure test platforms, determining the pressure associated test points of the pressure target test platform based on the pressure topological structure graph, performing a similarity comparison between all the pressure associated test points and the data tested by the pressure target test platform, and judging whether to use the pressure target test platform as a data rationality test point according to the comparison result; and / or, calibrating the water absorption test platform as a data rationality point as a water absorption target test platform, establishing a water absorption topological structure graph based on all the water absorption test platforms; determining the water absorption associated test points of the water absorption target test platform based on the water absorption topological structure graph; performing a similarity comparison between all the water absorption associated test points and the data tested by the water absorption target test platform, and judging whether to use the water absorption target test platform as a data rationality test point according to the comparison result.

[0041] Step S600 indicates that the corresponding test platform obtained by step S500, which can be used as a data rationality point, may have two test functions that are normal or abnormal at the same time. In order to further identify, further longitudinal comparison is adopted. Taking the pressure test platform as a data rationality point as an example, it is calibrated as a pressure target test platform, and a topological relationship is established between the pressure target test platform and the remaining pressure test platforms to obtain a pressure topological structure diagram. Based on the pressure topological structure diagram, the remaining pressure test platforms directly associated with the pressure target test platform are found and used as pressure-related test points. The soft rock compressive strength data of one or more pressure-related test points are compared with the soft rock compressive strength data of the pressure target test platform for similarity. The data is compared by using direct association, that is, two test points that are directly connected in the actual underground environment. The principle of connectivity that the soft rock compressive strength data theoretically remain consistent is used to further identify whether the data test of the pressure target test platform is normal. The water absorption rate target test platform is processed in the same way, which will not be repeated here.

[0042] By conducting the above-mentioned longitudinal comparison of the data of the pressure target test platform or the water absorption target test platform separately or simultaneously, it is possible to further identify whether the pressure target test platform or the water absorption target test platform has test abnormalities, thereby determining whether it can be used as a data rationality test point. For example, if it is found that the data similarity between one of the pressure target test platforms and multiple pressure-related test points is low, the test data of the pressure target test platform will be excluded to ensure that the data source for subsequent analysis has further reliability.

[0043] In this embodiment, the similarity comparison mainly performs differential analysis through smaller or finer indicators, and the similarity comparison includes the following steps:

[0044] S610: Determine the means for acquiring test data, wherein the acquisition means include chemical testing, physical testing and biological testing; this step indicates determining the method for acquiring test data of corresponding associated test points (pressure associated test points or water absorption associated test points) and corresponding target test platforms (pressure target test platforms or water absorption target test platforms), wherein there are mainly three related methods, chemical testing such as testing inorganic pollutants, organic pollutants, heavy metal ions, etc. in soft rocks; physical testing such as testing the conductivity and turbidity of water bodies; biological testing such as testing the toxic effects of pollutants and potential threats to soft rocks.

[0045] Then, step S620 is performed: determine the weights of each sub-item in the acquisition means, and calculate the distance coefficient between the weights of each sub-item in the acquisition means of the corresponding associated test point and the weights of each sub-item in the acquisition means of the corresponding target test platform; this step represents the use of the test data sub-items between the corresponding associated test point and the corresponding target test platform for comparison, and further finds the comparison of indicators of the specific similarity test, wherein the principle of expressing the high and low similarity in the form of the distance between the corresponding data is used to further obtain the judgment result of whether the corresponding target test platform has an abnormal test. Thus, step S640 is performed: obtain the central tendency of all distance coefficients, and calculate the similarity comparison result between the corresponding associated test point and the corresponding target test platform based on the central tendency. This step represents observing and analyzing all distance coefficients, selecting the median based on the concentration, and then judging the similarity based on the median selected by the concentration. For example, if the distance coefficient represented by the median is greater than 0.05, it means that the similarity is not high, otherwise it means that the degree of similarity is acceptable, so that the corresponding target test platform is used as the data rationality point.

[0046] On the basis of the above technical solution, the following steps are also included before obtaining the central tendency of all distance coefficients

[0047] S630: Arrange all distance coefficients to obtain a pressure sequence; assign a basic value to each sub-item in the pressure sequence, and then assign a correction value to each of the basic values ​​based on the corresponding topological structure diagram, which means that it is necessary to perform a comprehensive assignment in combination with the distance between the nodes in the topological structure diagram to obtain multiple correction results, and determine the distance coefficients corresponding to the first P correction results in the multiple correction results to perform subsequent steps, where P is a positive integer. This step indicates that when calculating the concentration of the distance coefficient, it is necessary to consider the number of nodes between the corresponding associated test point and the target test platform. The number of nodes is obtained through the corresponding topological structure diagram. If the number of nodes is larger, the correction value is larger. The correction value difference is merged with the basic value, so that the obtained correction result is closer to the actual situation. Among them, the relationship between the correction value and the basic value can be obtained through a theoretical model formed based on historical data, or it can be assigned through experience. However, if a large distance coefficient appears in the final correction result, it will be excluded as an abnormal point to ensure that the concentration calculation is more accurate. In other words, the value of P mainly depends on the abnormality of the distance coefficient, and the distance coefficient with a variance greater than the specified value can be eliminated.

[0048] It should be noted that, based on the above scheme, there may be real and correct distance coefficients among the eliminated abnormal distance coefficients, which need to be supplemented into the concentration measurement process to obtain a more objective and reasonable basis for constructing data comparison, that is, the correction result corresponding to the abnormal distance coefficient is supplemented when determining the P correction results, wherein the abnormal distance coefficient is obtained by fitting all distance coefficients. By fitting all distance coefficients, the value of the distance coefficient is used as the horizontal coordinate, and the corresponding node position is used as the vertical coordinate for fitting to find the abnormal distance coefficient. If the value of the abnormal distance coefficient is large, but the node, that is, the coordinate, is close to the corresponding target test platform, the node position is determined by fitting the curve, and it can be used as the supplement object.

[0049] In the actual similarity comparison calculation, there will be situations where the basic environment between adjacent nodes is different, especially for soft rock compressive strength data. In a geographical environment with certain faults or exploration between two nodes, the soft rock compressive strength data of the two nodes cannot be calculated through the connectivity principle to calculate the similarity of the test data. At this time, the following steps are also required, that is, after determining the pressure-related test points of the pressure target test platform based on the pressure topology diagram, the following steps are also included: obtaining the relative exploration parameters between the pressure target test platform and the pressure-related test points; and eliminating the pressure-related test points whose relative exploration parameters exceed the pressure preset value. This step represents the elimination of the pressure-related test points with relatively large exploration between the pressure-related test points and the pressure target test platform, and does not serve as the basis for subsequent similarity comparisons to ensure the rationality of data analysis. The pressure preset value is predetermined and can be sufficiently small while ensuring the calculation accuracy.

[0050] On the basis of the above technical solution, considering that once all pressure-related test points exceed the preset pressure value, there will be too few samples of pressure-related test points, which is not conducive to comprehensively obtaining more reasonable data similarity comparison results.

[0051] That is, the pressure-related test points that may cause greater test data errors due to exploration can be eliminated, and the rest can be filled in. Then, the elimination of the pressure-related test points whose relative exploration parameters exceed the preset pressure value also includes the following steps: determining the number of nodes Q between the eliminated pressure-related test points and the pressure target test platform based on the pressure topological structure diagram, and if the Q value is less than or equal to the preset water absorption rate value, the eliminated pressure-related test points are filled in as the basis for comparison with the pressure target test platform.

[0052] This step means that the pressure-related test points with distant nodes but reasonable exploration are added, and the pressure-related test points with close nodes but relatively serious exploration are kept removed. The preset value of water absorption rate is also predetermined, and can be sufficiently large while ensuring that the calculation meets the limit. On the basis of this scheme, in order to further obtain a more accurate data calculation basis, the added pressure-related test points are assigned a mechanical distribution coefficient, and the mechanical distribution coefficient is used as the calculation basis for substituting the pressure-related test points for similarity comparison. That is, it means that the mechanical distribution coefficient is converted from reasonable exploration (such as relative exploration caused by terrain difference), substituted as a weight and assigned to the distance coefficient, so as to have a more reliable and reasonable data calculation basis when performing similarity comparison calculations.

[0053] This embodiment also provides a method for obtaining and testing soft rock mechanical parameters based on big data, which is mainly used to divide the functional modules of the method for obtaining and testing soft rock mechanical parameters based on big data according to the embodiment of the above method. For example, each functional module can be divided, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the present invention is schematic, which is only a logical function division, and there may be other division methods in actual implementation. For example, in the case of dividing each functional module according to each function, the method for obtaining and testing soft rock mechanical parameters based on big data may include a pressure division module, a pressure calculation module, a water absorption calculation module, a pressure matching module and a pressure judgment module. The functions of each unit module are described below.

[0054] A pressure division module, which is used to divide the soft rock distribution area into rock layers, obtain pressure parameter acquisition nodes and water absorption parameter acquisition nodes, wherein the pressure parameter acquisition node is used to test the compressive strength of soft rocks in the soft rock distribution area, and the water absorption parameter acquisition node is used to test the water absorption of soft rocks in the soft rock distribution area; a pressure calculation module, which is used to obtain all pressure test platforms in the pressure parameter acquisition node, and determine the test data of all the pressure test platforms to form a pressure test data group, and calculate the maximum value of each test data in the pressure test data group to obtain a pressure change line graph; a water absorption rate calculation module, which is used to obtain all water absorption rate test platforms in the water absorption rate parameter acquisition node , and determine the test data of all the water absorption test platforms to form a water absorption test data group, and calculate the maximum value of each test data in the water absorption test data group to obtain a water absorption change line graph; a pressure matching module, which is used to match the pressure test platform and the water absorption test platform based on the zoning attributes, and determine the respective maximum values ​​of the paired pressure test platform and the water absorption test platform and make a judgment; a pressure judgment module, which is used to use the pressure test platform and the water absorption test platform as data rationality test points if the time node at which the maximum value of the pressure test platform in the pressure change line graph is located is consistent with the time node at which the maximum value of the water absorption test platform in the water absorption change line graph is located.

[0055] In some embodiments, the big data-based soft rock mechanical parameter acquisition and testing method also includes a water absorption rate judgment module, which is used to calibrate the pressure test platform as a data rationality point as a pressure target test platform, establish a pressure topology structure diagram based on all the pressure test platforms, determine the pressure-related test points of the pressure target test platform based on the pressure topology structure diagram, perform a similarity comparison between all the pressure-related test points and the data tested by the pressure target test platform, and judge whether to use the pressure target test platform as a data rationality test point according to the comparison result; and / or, calibrate the water absorption rate test platform as a data rationality point as a water absorption rate target test platform, establish a water absorption rate topology structure diagram based on all the water absorption rate test platforms; determine the water absorption rate associated test points of the water absorption rate target test platform based on the water absorption rate topology structure diagram; perform a similarity comparison between all the water absorption rate associated test points and the data tested by the water absorption rate target test platform, and judge whether to use the water absorption rate target test platform as a data rationality test point according to the comparison result.

[0056] In some embodiments, the water absorption rate judgment module is also used to determine the acquisition means of the test data, wherein the acquisition means include chemical testing, physical testing and biological testing; determine the proportion between each sub-item in the acquisition means, calculate the distance coefficient between the proportion of each sub-item in the corresponding associated test point acquisition means and the proportion of each sub-item in the corresponding target test platform acquisition means; arrange all distance coefficients to obtain a pressure sequence; assign a basic value to each sub-item in the pressure sequence, and then assign a correction value to each of the basic values ​​based on the corresponding topological structure diagram to obtain multiple correction results, determine the distance coefficient corresponding to the first P correction results in the multiple correction results to perform subsequent steps; obtain the central tendency of all distance coefficients, and calculate the similarity comparison result between the corresponding associated test point and the corresponding target test platform based on the central tendency. And obtain the relative exploration parameter between the pressure target test platform and the pressure-associated test point; remove the pressure-associated test points whose relative exploration parameters exceed the preset pressure value.

[0057] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for acquiring and testing soft rock mechanical parameters based on big data, characterized in that: The steps include: Divide the soft rock distribution area into rock layers to obtain pressure parameter acquisition nodes and water absorption rate parameter acquisition nodes, wherein the pressure parameter acquisition nodes are used to test the compressive strength of soft rocks in the soft rock distribution area, and the water absorption rate parameter acquisition nodes are used to test the water absorption rate of soft rocks in the soft rock distribution area; Acquire all pressure test platforms in the pressure parameter acquisition node, determine the test data of all the pressure test platforms, form a pressure test data group, calculate the maximum value of each test data in the pressure test data group, and obtain a pressure change line graph; Acquire all water absorption test platforms in the water absorption parameter acquisition node, determine the test data of all the water absorption test platforms, form a water absorption test data group, calculate the maximum value of each test data in the water absorption test data group, and obtain a water absorption change line graph; Matching the pressure test platform and the water absorption test platform based on the zoning attributes, determining the respective maximum values ​​of the paired pressure test platform and the water absorption test platform and making a judgment; If the time node at which the maximum value of the pressure test platform in the pressure change line graph is located is consistent with the time node at which the maximum value of the water absorption rate test platform in the water absorption rate change line graph is located, then the pressure test platform and the water absorption rate test platform are used as data rationality test points.

2. The method for obtaining and testing soft rock mechanical parameters based on big data according to claim 1 is characterized in that: The pressure test platform as a data rationality point is calibrated as a pressure target test platform, a pressure topology structure diagram is established based on all the pressure test platforms, the pressure-related test points of the pressure target test platform are determined based on the pressure topology structure diagram, all the pressure-related test points are compared with the data tested by the pressure target test platform for similarity, and whether the pressure target test platform is used as a data rationality test point is determined according to the comparison result; The water absorption test platform serving as a data rationality point is calibrated as a water absorption target test platform, and a water absorption topology structure diagram is established based on all the water absorption test platforms; the water absorption associated test points of the water absorption target test platform are determined based on the water absorption topology structure diagram; all water absorption associated test points are compared with the data tested by the water absorption target test platform for similarity, and whether to use the water absorption target test platform as a data rationality test point is determined based on the comparison results.

3. The method for obtaining and testing soft rock mechanical parameters based on big data according to claim 2 is characterized in that: After determining the pressure-associated test point of the pressure target test platform based on the pressure topological structure diagram, the following steps are also included: Relative exploration parameters between the pressure target test platform and the pressure-associated test points are obtained; and pressure-associated test points whose relative exploration parameters exceed preset pressure values ​​are eliminated.

4. The method for obtaining and testing soft rock mechanical parameters based on big data according to claim 3 is characterized in that: The method of eliminating pressure-related test points whose relative exploration parameters exceed the preset pressure value also includes the following steps: determining the number of nodes Q between the eliminated pressure-related test points and the pressure target test platform based on the pressure topology structure diagram; if the Q value is greater than or equal to the preset water absorption rate value, the eliminated pressure-related test points are added as the basis for comparison with the pressure target test platform.

5. The method for acquiring and testing soft rock mechanical parameters based on big data according to claim 4 is characterized in that: The added pressure-related test point is assigned a mechanical distribution coefficient, and the mechanical distribution coefficient is used as the calculation basis for substituting the pressure-related test point for similarity comparison.

6. The method for acquiring and testing soft rock mechanical parameters based on big data according to any one of claims 1 to 5, characterized in that: include: A pressure division module, which is used to divide the soft rock distribution area into rock layers, and obtain a pressure parameter acquisition node and a water absorption rate parameter acquisition node, wherein the pressure parameter acquisition node is used to test the compressive strength of soft rocks in the soft rock distribution area, and the water absorption rate parameter acquisition node is used to test the water absorption rate of soft rocks in the soft rock distribution area; A pressure calculation module, which is used to obtain all pressure test platforms in the pressure parameter acquisition node, and determine the test data of all the pressure test platforms to form a pressure test data group, and calculate the maximum value of each test data in the pressure test data group to obtain a pressure change line graph; A water absorption rate calculation module, which is used to obtain all water absorption rate test platforms in the water absorption rate parameter acquisition node, and determine the test data of all the water absorption rate test platforms to form a water absorption rate test data group, and calculate the maximum value of each test data in the water absorption rate test data group to obtain a water absorption rate change line graph; A pressure matching module, which is used to match the pressure test platform and the water absorption test platform based on the zoning attributes, determine the respective maximum values ​​of the paired pressure test platform and the water absorption test platform, and make a judgment; The pressure judgment module is used to judge: if the time node at which the maximum value of the pressure test platform in the pressure change line graph is located is consistent with the time node at which the maximum value of the water absorption rate test platform in the water absorption rate change line graph is located, then the pressure test platform and the water absorption rate test platform are used as data rationality test points.

7. The method for acquiring and testing soft rock mechanical parameters based on big data according to claim 6 is characterized in that: It also includes a water absorption rate judgment module, which is used to: The pressure test platform as a data rationality point is calibrated as a pressure target test platform, a pressure topology structure diagram is established based on all the pressure test platforms, the pressure-related test points of the pressure target test platform are determined based on the pressure topology structure diagram, all the pressure-related test points are compared with the data tested by the pressure target test platform for similarity, and whether the pressure target test platform is used as a data rationality test point is determined according to the comparison result; The water absorption test platform serving as a data rationality point is calibrated as a water absorption target test platform, and a water absorption topology structure diagram is established based on all the water absorption test platforms; the water absorption associated test points of the water absorption target test platform are determined based on the water absorption topology structure diagram; all water absorption associated test points are compared with the data tested by the water absorption target test platform for similarity, and whether to use the water absorption target test platform as a data rationality test point is determined based on the comparison results.

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