CN111911132B - Evaluation system and method for evaluating rock mass grade based on change of impact acceleration - Google Patents

Abstract

The invention discloses an evaluation system and method for evaluating rock mass grades based on changes in impact acceleration. The evaluation system includes at least one acceleration signal sensitive element, an IoT gateway and a server; the server is configured to receive acceleration signals of at least two vibration cycles, and obtain the peak acceleration of the impact segment of each vibration cycle according to the acceleration signals, and the server obtains adjacent two vibration cycles. The acceleration difference between the peak accelerations of the impact section of each vibration period; the server evaluates the rock mass grade of the rock mass to be tested according to at least one acceleration difference and an evaluation model configured previously. The evaluation system of this embodiment can collect the acceleration of the impact section of each vibration period when the rock drill is drilling multiple times in a row, and evaluate the rock mass grade of the rock mass to be tested according to the difference in the acceleration of the impact section between adjacent vibration periods.

Description

Evaluation system and method for evaluating rock mass grade based on change of impact acceleration

technical field

The invention relates to the field of geotechnical mechanics, in particular to an evaluation system and method for evaluating rock mass grades based on changes in impact acceleration.

Background technique

During the drilling process, the drilling is in direct contact with the rock and soil mass. The response information of the drilling rig comprehensively reflects the mechanical properties of the rock and soil mass. A large amount of geological data hidden in the response information of the rock drilling rig can be used to analyze and measure the mechanical parameters of the rock and soil mass. It is an important reference index for stratigraphic interface identification and surrounding rock grade division. At present, the rig response information in the field of geotechnical engineering has not been fully collected and applied.

SUMMARY OF THE INVENTION

The embodiments of the present invention disclose at least one evaluation system for evaluating rock mass grades based on changes in impact acceleration. The evaluation system of this embodiment can collect the acceleration of the impact section of each vibration period when the rock drill is drilling multiple times in a row, and evaluate the rock mass grade of the rock mass to be tested according to the difference in the acceleration of the impact section between adjacent vibration periods.

In order to achieve the above content, the evaluation system in this embodiment includes at least one acceleration signal sensing element, an IoT gateway and a server; the acceleration signal sensing element is configured to be installed in the drilling direction of the rock drill, and obtains a The acceleration when drilling the rock mass to be tested, the acceleration sensing element generates an acceleration signal according to the acceleration; the acceleration signal sensing element sends the acceleration signal to the server through the IoT gateway; the server is configured In order to receive the acceleration signals of at least two vibration cycles, and obtain the peak acceleration of the impact segment of each vibration cycle according to the acceleration signals, the server obtains the acceleration difference between the peak accelerations of the impact segment of two adjacent vibration cycles value; the server evaluates the rock mass grade of the rock mass to be tested according to at least one of the acceleration difference values and a previously configured evaluation model.

In some embodiments disclosed in the present invention, the peak acceleration of the impact segment includes a maximum acceleration of the impact segment and a minimum acceleration of the impact segment; the server obtains the maximum acceleration difference between the maximum accelerations of the impact segment in two adjacent vibration periods, and obtaining the minimum acceleration difference between the minimum accelerations of the impact segment; the server evaluates the rock mass to be tested according to the combination of at least one of the maximum acceleration difference and the minimum acceleration difference and the previously configured evaluation model of the rock mass grade.

In some embodiments disclosed in the present invention, the server is configured to receive the acceleration signals of at least two vibration cycles, and obtain the average acceleration of the impact segment for each vibration cycle according to the acceleration signals, and the server obtains the corresponding acceleration signals. The mean acceleration difference between the mean accelerations of the impact segments of two adjacent vibration periods; the server according to the combination of the maximum acceleration difference, the maximum acceleration difference and the mean acceleration difference in at least one vibration period, and the The previously configured evaluation model evaluates the rock mass grade of the rock mass to be tested.

In some embodiments disclosed in the present invention, the server obtains the mean acceleration of the impact segment for n vibration periods, and obtains n-1 values of the maximum acceleration difference, the maximum acceleration difference, and the mean acceleration difference. combination; the server constructs a data matrix according to n-1 combinations; the server evaluates the rock mass grade of the rock mass to be tested according to the data matrix and the evaluation model.

In some embodiments disclosed in the present invention, the server reduces the dimension of the data matrix to an input matrix through principal component analysis; the server evaluates the rock mass of the rock mass to be tested according to the input matrix and the evaluation model body level.

In some embodiments disclosed in the present invention, the server configures the evaluation model to: obtain the acceleration signal of at least one prior rock mass in n vibration cycles; obtain n-1 vibration cycles according to the acceleration signal The maximum acceleration difference, the maximum acceleration difference and the average acceleration difference; the combination of n-1 maximum acceleration difference, the maximum acceleration difference and the average acceleration difference is the sample matrix input, select the first The rock mass grade of the tested rock mass is the sample output; the evaluation model is trained according to the combination of the sample matrix input and the sample output.

In some embodiments disclosed in the present invention, the evaluation model configured by the server includes an input layer, a hidden layer and an output layer; the server randomly configures the weights of the input layer, the hidden layer and the output layer; The sample matrix input of the rock mass is used as the input of the evaluation model to obtain the actual output of the evaluation model; the back-pass algorithm is used to compare the actual output with the sample output, which is used to update the evaluation model. The weights of the input layer, hidden layer and output layer.

The embodiments of the present invention disclose at least one evaluation method for evaluating rock mass grades based on changes in impact acceleration. The method includes: acquiring the acceleration signals of at least two vibration cycles, and acquiring the peak acceleration of the impact segment of each vibration cycle according to the acceleration signals, and the server acquiring the sum of the peak accelerations of the impact segment of two adjacent vibration cycles. and evaluating the rock mass grade of the rock mass to be tested according to at least one of the acceleration difference values and a previously configured evaluation model.

In some embodiments disclosed in the present invention, the obtained peak acceleration of the impact segment is the maximum acceleration of the impact segment and the minimum acceleration of the impact segment; the maximum acceleration difference between the maximum accelerations of the impact segment of two adjacent vibration periods is obtained, and Obtaining the minimum acceleration difference between the minimum accelerations of the impact section; evaluating the rock mass of the rock mass to be tested according to the combination of at least one of the maximum acceleration difference and the minimum acceleration difference and the previously configured evaluation model body level.

In some embodiments disclosed in the present invention, the mean acceleration difference between the mean accelerations of the impact segments of two adjacent vibration periods is obtained; according to at least one of the maximum acceleration difference, the maximum acceleration difference and the mean acceleration difference The combination of values and the previously configured evaluation model evaluates the rock mass grade of the rock mass to be tested.

In view of the above solutions, the present invention will be described in detail below with reference to the accompanying drawings to the disclosed exemplary embodiments, which will also make other features and advantages of the embodiments of the present invention clear.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore do not It should be regarded as a limitation of the scope, and for those of ordinary skill in the art, other related drawings can also be obtained according to these drawings without any creative effort.

Figure 1 shows the topology of the evaluation system;

Fig. 2 is the acceleration curve diagram of the three consecutive drilling holes of the rock drill in the example of the evaluation system;

FIG. 3 is a flow chart of the execution steps of the evaluation method.

Detailed ways

Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. The following detailed description sets forth numerous specific details in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various embodiments and the appended claims, the singular forms "a" ("a", "an") and "the" are intended to include the plural forms as well, unless the context dictates otherwise clearly instructed. It will also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will also be understood that the terms "includes", "including", "comprises" and/or "comprising" when used in this specification are intended to designate the presence of stated features, integers, steps, operations, elements and/or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groupings thereof.

This embodiment discloses an evaluation system for evaluating rock mass grades based on changes in impact acceleration.

FIG. 1 shows the topology of the evaluation system of this embodiment, which specifically includes an acceleration sensor, an IoT gateway, and a server. In this topology, the acceleration sensor communicates with the server through the IoT gateway.

The acceleration sensor in this embodiment is installed in the drilling direction of the rock drill, and is only used to obtain the vibration acceleration of the rock drill when the rock drill is drilling. According to the drilling process of the rock drill, the vibration acceleration of each stage is divided into the acceleration of the impact section, the acceleration of the return section and the acceleration of the return section. Among them, the acceleration of the impact section is the acceleration of the vibration of the rock drill in the drilling direction when the rock drill hits the rock mass and the drill pipe contacts the bottom of the rock mass to break the rock. The acceleration of the return section is the acceleration of the vibration of the rock drill in the drilling direction when the drill pipe is separated from the bottom of the rock mass after the rock drill impacts the rock mass and returns in the reverse direction along the drilling direction. The acceleration of the strikeback segment is the acceleration of the rock drill vibrating in the drilling direction when the rock drill accelerates and impacts toward the bottom of the rock mass after the rock drill returns. The application of the acceleration of the impact segment in the evaluation system of this embodiment.

The IoT gateway in this embodiment has various communication interfaces and protocols, and is used for receiving acceleration signals sent by the acceleration sensor; meanwhile, all the above-mentioned acceleration signals are modulated and then sent to the server.

The server in this embodiment generally includes a memory and a processor. The memory mainly includes a stored program area and a stored data area; wherein, the stored program area can store an operating system, an application program required by at least one function, and a program involved in this embodiment. And, the storage data area can store data created according to the use of the terminal, including relevant setting information or usage information of the application displayed on the display screen involved in this embodiment. Additionally, memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, and other volatile solid state storage devices. The processor provides high-speed computing capability and can call and execute programs stored in memory. In this embodiment, the server is configured in the computer room, and is configured and maintained by professional operation and maintenance personnel, and is used to communicate with the IoT gateway remotely through a wired link, and is used to receive the modulation acceleration signal of the IoT gateway.

The above is the hardware composition of the evaluation system of this embodiment; on the basis of this hardware composition, the principle of the evaluation system of this embodiment is based on the fact that during the process of drilling multiple times by the rock drill, the adjacent single drilling, that is, the difference between the vibration periods The correlation between the change in the acceleration of the impact segment and the quality of the rock mass. Specifically, it is shown in Fig. 2 that the acceleration of the impact section obtained by the acceleration sensor has a numerical change during the three consecutive advancements of the rock drill. The reason for the numerical change is that during the drilling process of the rock drill, the rock drill continues A drill point in the drilling direction breaks the rock and extends into the hole. During the process of breaking the rock and extending into the hole, the drill pipe of the rock drill acts on the bottom of the current rock drilling point to generate vibration, and the vibration is affected by the rock at the current drilling point. Body mass effect.

Specifically, the server in this embodiment acquires the acceleration signals of n vibration periods (n≥2 and is a positive integer) modulated by the IoT gateway, and acquires, according to the acceleration signals, the maximum acceleration of the impact segment and the minimum impact segment in each vibration cycle Acceleration and the mean acceleration of the impact segment.

After obtaining the maximum acceleration of the impact segment, the minimum acceleration of the impact segment, and the average acceleration of the impact segment for n consecutive vibration cycles, the server obtains the maximum acceleration difference according to the difference between the maximum accelerations of the impact segment of two adjacent vibration cycles, and the minimum acceleration of the impact segment. The difference of the minimum acceleration difference is obtained, and the difference of the average acceleration of the impact section is obtained to obtain the average acceleration difference. Then the server can input data sets of n-1 maximum acceleration difference, minimum acceleration difference and mean acceleration difference.

The server constructs a data matrix according to n-1 input data sets, and then selects the principal component analysis method to reduce the dimension of the data matrix as an input matrix. The server selects the input matrix as the input data of a previously configured evaluation model, and the evaluation model evaluates the rock mass grade of the rock mass to be tested according to the input data.

The input data set is used as the input of a previously trained evaluation model; the rock mass grade of the rock mass to be tested is evaluated by the evaluation model.

In this embodiment, the server configuration evaluation model is to obtain acceleration signals of several prior rock masses in n vibration periods;

Obtain the maximum acceleration difference, the maximum acceleration difference and the average acceleration difference of n-1 vibration cycles according to the acceleration signal;

The combination of n-1 maximum acceleration difference, the maximum acceleration difference and the mean acceleration difference is the sample matrix input, and the rock mass grade of the prior rock mass is selected as the sample output;

The evaluation model is trained according to the combination of sample matrix input and sample output.

Preferably, the evaluation model configured by the server includes an input layer, a hidden layer and an output layer. Among them, the random configuration weights of the initial input layer, hidden layer and output layer. For the configuration of the evaluation model, the server mainly selects the sample matrix of a number of prior rock masses as the sample input of the untrained evaluation model, the rock mass grade as the sample output of the untrained evaluation model, and the combination of several sample inputs and sample outputs. for training and evaluating models. At the same time, in the process of training the evaluation model, the server repeatedly uses the evaluation model under training to obtain the actual output associated with a certain input data, and then selects the back-pass algorithm to compare the actual output and the sample output, which is used to update the input layer, hidden layer in the evaluation model. The weights of the containing layer and the output layer.

Based on the specific description of the above evaluation system, this embodiment further discloses an evaluation method for evaluating rock mass grades based on changes in impact acceleration. The steps of the evaluation method are executed in the server in the form of instructions. When the server executes the instruction, the steps shown in FIG. 3 are implemented.

S100 acquires the acceleration signals of n vibration periods, and acquires the maximum acceleration of the impact segment, the minimum acceleration of the impact segment, and the mean value and speed of the impact segment of each vibration period according to the acceleration signals.

S200 acquires the maximum acceleration difference between the maximum accelerations of the impact segment, the minimum acceleration difference between the minimum accelerations of the impact segment, and the mean acceleration difference between the mean accelerations of the impact segment in every two adjacent vibration periods.

S300 constructs a data matrix according to the combination of n-1 maximum acceleration differences, maximum acceleration differences and average acceleration differences.

S400 selects the principal component analysis method to reduce the dimension of the data matrix as the input matrix.

S500 evaluates the rock mass grade of the rock mass to be tested associated with the input matrix through the previously configured evaluation model.

The above are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (4)

1. an evaluation system for evaluating rock mass grades based on changes in impact acceleration, is characterized in that, The evaluation system includes at least one acceleration signal sensing element, an IoT gateway and a server; The acceleration signal sensing element is configured to be installed in the drilling direction of the rock drilling machine, and acquires the acceleration of the rock drilling machine when drilling a rock mass to be tested, and the acceleration sensing element generates an acceleration signal according to the acceleration; The acceleration signal sensitive element sends the acceleration signal to the server through the IoT gateway; The server is configured to receive the acceleration signals of at least two vibration cycles, and obtain, according to the acceleration signals, a peak acceleration of the impact segment for each vibration cycle, the peak acceleration of the impact segment including the maximum acceleration of the impact segment and the minimum impact segment of the impact segment. Acceleration and mean acceleration of impact segment; The server obtains the maximum acceleration difference between the maximum accelerations of the impact segments in two adjacent vibration periods, the minimum acceleration difference between the minimum accelerations of the impact segments, and the mean acceleration difference between the mean accelerations of the impact segments ; obtaining, by the server, the mean acceleration of the impact segment for n vibration periods, and obtaining n-1 combinations of the maximum acceleration difference, the minimum acceleration difference and the mean acceleration difference; The server constructs a data matrix according to n-1 combinations; The server evaluates the rock mass grade of the rock mass to be tested according to the data matrix and the evaluation model; The server configures the evaluation model as: acquiring the acceleration signal of at least one prior rock mass in n vibration periods; Obtain the maximum acceleration difference value, the minimum acceleration difference value and the average acceleration difference value of n-1 vibration cycles according to the acceleration signal; Combining n-1 combinations of the maximum acceleration difference, the minimum acceleration difference and the mean acceleration difference as the sample matrix input, and selecting the rock mass grade of the prior rock mass as the sample output; The evaluation model is trained from a combination of the sample matrix input and the sample output. 2. The evaluation system for evaluating rock mass grades based on changes in impact acceleration as claimed in claim 1, characterized in that, The server reduces the dimension of the data matrix by principal component analysis to an input matrix; The server evaluates the rock mass grade of the rock mass to be tested according to the input matrix and the evaluation model. 3. The evaluation system for evaluating rock mass grades based on changes in impact acceleration as claimed in claim 1, characterized in that, The evaluation model configured by the server includes an input layer, a hidden layer and an output layer; The server randomly configures the weights of the input layer, the hidden layer and the output layer; Selecting the sample matrix input of the prior rock mass as the input of the evaluation model to obtain the actual output of the evaluation model; A back-pass algorithm is used to compare the actual output with the sample output, so as to update the weights of the input layer, the hidden layer and the output layer in the evaluation model. 4. An evaluation method for evaluating rock mass grades based on changes in impact acceleration, characterized in that, The method includes: Acquire the acceleration signals of at least two vibration periods, and acquire the peak acceleration of the impact segment of each vibration period according to the acceleration signals, and the server acquires the acceleration difference between the peak accelerations of the impact segment of two adjacent vibration periods; Acquire the acceleration signals of at least two vibration cycles, and obtain the peak acceleration of the impact segment for each vibration cycle according to the acceleration signals, where the peak acceleration of the impact segment includes the maximum acceleration of the impact segment, the minimum acceleration of the impact segment, and the average acceleration of the impact segment ; Obtain the maximum acceleration difference between the maximum accelerations of the impact segments in two adjacent vibration periods, obtain the minimum acceleration difference between the minimum accelerations of the impact segment, and obtain the average acceleration difference between the average accelerations of the impact segments; obtaining the mean acceleration of the impact segment for n vibration periods, and obtaining n-1 combinations of the maximum acceleration difference, the minimum acceleration difference and the mean acceleration difference; Build a data matrix according to n-1 combinations; Evaluate the rock mass grade of the rock mass to be tested according to the data matrix and the evaluation model; Wherein, the evaluation model is: acquiring the acceleration signal of at least one prior rock mass in n vibration periods; Obtain the maximum acceleration difference value, the minimum acceleration difference value and the average acceleration difference value of n-1 vibration cycles according to the acceleration signal; Combining n-1 combinations of the maximum acceleration difference, the minimum acceleration difference and the mean acceleration difference as the sample matrix input, and selecting the rock mass grade of the prior rock mass as the sample output; The evaluation model is trained from a combination of the sample matrix input and the sample output.

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