CN117868782A - Method for optimizing drilling and blasting parameters based on rotational speed of pneumatic drill rod - Google Patents
Method for optimizing drilling and blasting parameters based on rotational speed of pneumatic drill rod Download PDFInfo
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- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
- E21B44/02—Automatic control of the tool feed
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Abstract
The invention discloses a method for optimizing drilling and blasting parameters based on the rotating speed of an air drill rod, which belongs to the technical field of construction blasting, and comprises the following steps of S1, measuring the rotating speed of the drill rod by using a sensor module, S2, uploading measured data to a cloud by using an Internet of things module and a network optimization module; s3, analyzing, processing and predicting stored data by using a time sequence autoregressive moving average model to realize drilling and blasting parameter optimization; s4, outputting and feeding back; according to the method, drill hole data are collected in real time, time sequence autoregressive moving average model analysis processing and prediction data are used for realizing drill explosion parameter optimization, a worker adjusts the drill explosion parameters according to output results, and finally the results are fed back to the site to realize dynamic adjustment of the drill explosion parameters; the device can effectively ensure the photo-explosion effect, improve the photo-explosion efficiency, and has the characteristics of good parameter adjustment treatment effect, effectively ensure the photo-explosion effect, realize the dynamic adjustment of drilling and explosion parameters, achieve good economic benefit and the like.
Description
Technical Field
The invention relates to the technical field of construction blasting, in particular to a method for optimizing drilling and blasting parameters based on the rotating speed of an air drill rod.
Background
A pneumatic rock drill is an impact drilling machine using compressed air as power, which is called an air drill for short. The pneumatic drill explosion excavation is widely applied to mountain tunnel construction, the pneumatic drill explosion is widely used, the purchase and use cost is low, and the overexcavation is relatively easy to control. In summary, in the current domestic tunnel smooth blasting construction, wind drilling is still the mainstream rock drilling equipment.
Currently, the air drill is used for drilling holes only according to parameters designed by the existing blasting scheme, and the blasting parameters cannot be optimized continuously. However, the device is used for assisting in measuring the rotation speed of the drill rod; after the obtained drill rod rotating speed information is transmitted by the Internet of things module, a data transmission algorithm for optimizing link quality is used in the network optimization module, and rotating speed data are uploaded to the cloud; for the data stored in the cloud, the cloud system stores the drill rod rotating speeds of different sources and simultaneously adopts a time sequence autoregressive moving average model for analysis and prediction, and the method mainly comprises the following steps of: firstly, preprocessing data collected in the early stage, carrying out model order determination on a stable non-white noise sequence, constructing a model, then predicting the model to realize the correspondence between rotational speed data and surrounding rock classification, adopting different drilling and blasting parameters according to different surrounding rock grades, and providing a drilling and blasting parameter optimization scheme; and the workers evaluate according to the cloud output result, so that the dynamic adjustment of drilling and blasting parameters is realized, and better economic benefit is achieved. The drilling and blasting parameters can be adjusted after the drill is used for drilling each time, so that a better photo-blasting effect can be obtained. The invention provides a method, a device and a system for optimizing drilling and blasting parameters based on the rotating speed of an air drill rod, which have important significance for optimizing the drilling and blasting parameters in tunnel construction.
In order to solve the above problems, a method for optimizing drilling and blasting parameters based on the rotation speed of the pneumatic drill rod is needed to solve the problems in the prior art.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a method for optimizing drilling and blasting parameters based on the rotating speed of an air drill rod, and the method is used for realizing the optimization of drilling and blasting parameters by utilizing time sequence autoregressive moving average model analysis processing and prediction in a cloud system by adjusting the drilling and blasting parameters after each time of real-time use of the air drill, feeding back to a field professional, realizing the optimized adjustment of the drilling and blasting parameters according to cloud output results by workers, feeding back the results to a field blasting related unit, realizing the dynamic adjustment of the drilling and blasting parameters, and then being applied to subsequent drilling and blasting circulation; the device can effectively ensure the photo-explosion effect, improve the photo-explosion efficiency, and has the characteristics of good parameter adjustment treatment effect, effective guarantee of the photo-explosion effect, realization of dynamic adjustment of drilling and explosion parameters and better economic benefit.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a method for optimizing drilling and blasting parameters based on the rotation speed of an air drill rod comprises the following steps
S1, measuring the rotation speed of the drill rod by using a sensor module
When drilling, attaching the sensor module to the drill rod of the pneumatic drill, and measuring the rotating speed data of the drill rod;
s2, uploading measured data to a cloud by using an Internet of things module and a network optimization module;
s3, in the cloud system, analyzing, processing and predicting stored data by using a time sequence autoregressive moving average model to realize drilling and blasting parameter optimization;
s4, outputting and feeding back
Outputting the optimized drilling and blasting parameters, and performing optimization adjustment on the drilling and blasting parameters according to the output result, so that the dynamic adjustment of the drilling and blasting parameters is realized, and the drilling and blasting parameters are applied to subsequent drilling and blasting cycles.
Preferably, the internet of things module and the network optimization module in step S2 are integrated in the same circuit;
the internet of things module is used for sensing and collecting drill rod data and uploading drill rod rotating speed data to the internet cloud for storage;
the sensor comprises a rotation speed sensor and a sensor required by a network optimization module;
and the network optimization module is used for uploading the rotating speed data to the cloud end through wireless sensor network transmission by using a data transmission algorithm for optimizing the link quality.
Preferably, the wireless sensor network uses a wireless sensor network for data transmission, the wireless sensor network is a network formed by sensor nodes, and a plurality of sensors for sensing and checking the outside world in real time are distributed at the terminal of the wireless sensor network, and the processing process comprises the following steps of
1. Channel allocation of frequency random jump of the wireless sensor network is carried out, and the transmission rate of the wireless sensor network is improved;
2. detecting, processing and relieving the abnormal data to finish the detection of the abnormal data;
3. performing inter-cluster competitive transmission based on collision avoidance on abnormal data, and solving the interference of a clustering routing process;
4. congestion relief based on path transfer solves the congestion problem in the clustering routing process.
Preferably, the process of channel allocation for frequency random hopping of the wireless sensor network comprises
(1) During data transmission, dividing nodes in the WSN into a plurality of clusters, wherein a cluster head is responsible for data collection and calculation in the clusters and channel allocation process of the nodes in the clusters;
(2) All nodes have the same initial frequency, in a cluster collection stage, firstly, randomly selecting one frequency from a plurality of available frequencies to be distributed to a certain cluster head, then, secondarily distributing the frequency to all nodes in the cluster by the cluster head, and finally, completing the frequency distribution process of all nodes; the frequency formula allocated to the cluster head is:
f＝f 0 +cΔf
wherein: f (f) 0 Is the initial frequency of the node; c is a random number between (0, m-1), m is the number of available channels, and m is more than or equal to 0 and less than or equal to 16; Δf is a unit frequency step factor.
Preferably, the process of detecting, processing and canceling the abnormal data includes
(1) Firstly, defining an abnormal data threshold, and judging the abnormal data when the threshold is met;
(2) Reporting the determined position and data set of the abnormal data to a cluster head, and forwarding the position and the data set to a base station by the cluster head, wherein only the abnormal data set is transmitted in a network, and all other nodes are kept dormant;
(3) If no abnormal data is detected, the network is restored to the state that all nodes normally transmit data.
Preferably, the process of performing inter-cluster contention transmission based on collision avoidance and congestion relief based on path transfer on the abnormal data includes
Inter-cluster contention transmission procedure based on collision avoidance
Defining a competition access function, wherein the larger the competition access function is, the higher the priority of cluster head node access is, the priority of cluster head node access is sequenced by a receiving cluster head node, and when abnormal data exists in a data packet of the cluster head, the highest priority is given to the receiving cluster head, and the abnormal data packet is accessed and received;
congestion relief procedure based on path transfer
(1) Firstly, congestion detection is carried out on an accessed cluster head node, and the congestion condition is judged;
(2) Then setting a flag bit, and informing a source cluster head node of the congestion condition;
(3) And finally, the cluster head node in the congestion state is processed, and the congestion in the path is relieved.
Preferably, the process of analyzing, processing and predicting the data stored in the cloud end by using the time series autoregressive moving average model in the step S3 includes
S31, carrying out data preprocessing on the data stored in the cloud
The method comprises the steps of checking sequence stability and white noise, and if the obtained sequence does not meet one of the two, performing step S32;
s32, carrying out differential operation on the non-stationary sequence to obtain a stationary non-white noise sequence;
s33, after the stable non-white noise sequence is obtained, model scaling is carried out;
s34, constructing a model
The definition of the autoregressive-moving average model (ARMA) is:
the model constructs an ARMA (p, q) model for the fixed-order parameters;
s35, utilizing the model to carry out a plurality of time point results in the future;
s36, evaluating results
And (5) the rotation speed prediction data are corresponding to the surrounding rock grades, and the surrounding rock grades are output.
Preferably, the model scaling process of step S33 includes
According to the autoregressive model (AR), the definition of the p-th order autoregressive model is:
wherein y is t With current value, μ being a constant term, γ i As autocorrelation coefficient epsilon t Is an error
According to the moving average Model (MA), the definition of the q-order moving average model is:
wherein θ i Is a coefficient of sliding average
And searching optimal parameters p and q, and determining model parameters.
A device for optimizing drilling and blasting parameters based on the rotation speed of an air drill rod comprises
The battery supplies power for a device main board, and the device main board is an integrated board of the sensor module, the Internet of things module and the network optimization module;
the sensor modules are used for sensing and measuring the rotating speed of the air drill rod and are arranged on the Internet of things module;
the internet of things module is based on a cellular technology, is provided with the sensor and the network optimization module, and stores and transmits data acquired by the sensor;
and the network optimization module is internally used for uploading the rotating speed data to the cloud by using a data transmission algorithm for optimizing the link quality.
A system for optimizing drill burst parameters based on rotational speed of an air drill pipe, the system comprising a device end configured as means for performing a method of optimizing drill burst parameters based on rotational speed of an air drill pipe, a cloud end, and an output end.
The beneficial effects of the invention are as follows: the invention discloses a method for optimizing drilling and blasting parameters based on the rotating speed of an air drill rod, which is improved compared with the prior art in that:
1. the invention designs a method for optimizing drilling and blasting parameters based on the rotating speed of an air drill rod, which comprises the steps of measuring the rotating speed of the drill rod by using a sensor module, uploading measured data to a cloud end by using an Internet of things module and a network optimization module, analyzing, processing and predicting data stored in the cloud end by using a time sequence autoregressive moving average model, realizing the optimization, output and feedback of the drilling and blasting parameters, feeding back the result to a site blasting related unit, realizing the dynamic adjustment of the drilling and blasting parameters, and then applying the dynamic adjustment to a subsequent drilling and blasting cycle to ensure the drilling and blasting effect, thereby being capable of effectively ensuring the photo-blasting effect and improving the photo-blasting efficiency;
2. the invention designs a specific process of analyzing, processing and predicting data stored in a cloud by using a time sequence autoregressive moving average model, firstly, preprocessing data collected in the early stage, carrying out model scaling on a stable non-white noise sequence, constructing a model, then predicting the model to realize the correspondence of rotational speed data and surrounding rock classification, adopting different drilling and blasting parameters according to different surrounding rock grades, and providing a drilling and blasting parameter optimization scheme; the staff evaluates according to the cloud output result, realizes the correspondence of the rotational speed data and the surrounding rock classification, adopts different drilling and blasting parameters according to different surrounding rock grades, and proposes a drilling and blasting parameter optimization scheme; the operator realizes the optimized adjustment of the drilling and blasting parameters according to the cloud output result, and finally feeds the result back to the field blasting unit, so that the dynamic adjustment of the drilling and blasting parameters can be realized, better economic benefits are achieved, and the method has the advantages of good parameter adjustment treatment effect, effective guarantee of the photo-blasting effect, realization of the dynamic adjustment of the drilling and blasting parameters and better economic benefits.
Drawings
FIG. 1 is an algorithm flow chart of the method for optimizing drilling and blasting parameters based on the rotational speed of the pneumatic drill rod.
Fig. 2 is a flowchart of the operation of the wireless sensor network of the present invention.
Fig. 3 is a flow chart of a channel allocation procedure for frequency random hopping in accordance with the present invention.
FIG. 4 is a flow chart of the detection, processing and release process of the abnormal data according to the present invention.
Fig. 5 is a flowchart of a collision avoidance based inter-cluster contention transmission procedure according to the present invention.
Fig. 6 is a flow chart of a path transfer based congestion relief procedure of the present invention.
Fig. 7 is a block diagram of the apparatus for optimizing drill burst parameters based on rotational speed of an air drill pipe according to the present invention.
FIG. 8 is a flowchart of an algorithm for analyzing and predicting data using a time series autoregressive moving average model in accordance with the present invention.
Fig. 9 is a diagram of the best differential sequence of embodiment 2 of the present invention.
Fig. 10 is an Autocorrelation Chart (ACF) I of embodiment 2 of the present invention.
FIG. 11 is a Partial Autocorrelation Chart (PACF) I of example 2 of the present invention.
Fig. 12 is a diagram of the best differential sequence II of embodiment 2 of the present invention.
Fig. 13 is an Autocorrelation Chart (ACF) II of embodiment 2 of the present invention.
FIG. 14 is a Partial Autocorrelation Chart (PACF) II of example 2 of the present invention.
Detailed Description
In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1: referring to the method for optimizing drilling and blasting parameters based on the rotating speed of the pneumatic drill rod shown in the accompanying drawings 1-14, the design idea of the method is that the device is used for assisting in measuring the rotating speed of the drill rod when drilling each time; after the obtained drill rod rotating speed information is transmitted by the Internet of things module, a data transmission algorithm for optimizing link quality is used in the network optimization module, and rotating speed data are uploaded to the cloud; for data stored in the cloud, the cloud system stores drill rod rotating speeds of different sources and adopts a time sequence autoregressive moving average model (ARIMA) for analysis, processing and prediction, so that the rotating speed data corresponds to surrounding rock classification, different drilling and blasting parameters are adopted according to different surrounding rock grades, and a drilling and blasting parameter optimization scheme is provided; the staff realizes the optimized adjustment of the drilling and blasting parameters according to the cloud output result, and finally feeds back the result to a site blasting unit to realize the dynamic adjustment of the drilling and blasting parameters and guide the follow-up drilling and blasting process; the specific process comprises
S1, measuring the rotation speed of the drill rod by using a sensor module
Attaching a sensor module to the drill rod of the pneumatic drill during each drilling to measure the rotating speed data of the drill rod and obtain a rotating speed data set of the drill rod, wherein the sensor module is a plurality of rotating speed sensors;
s2, uploading measured data to the cloud by using an Internet of things module and a network optimization module
The Internet of things module and the network optimization module are integrated in the same circuit; wherein the method comprises the steps of
A plurality of sensors are distributed on the internet of things module and used for sensing and collecting data, and then the drill rod rotating speed data are uploaded to the internet cloud for storage, wherein the sensors comprise rotating speed sensors and sensors required by the network optimization module;
the network optimization module is used for uploading the rotating speed data to the cloud end by using a data transmission algorithm for optimizing the link quality in order to improve the transmission rate of the wireless sensor network and the stability of data transmission;
s3, in a cloud system, analyzing, processing and predicting data stored in the cloud by using a time sequence autoregressive moving average model to realize drilling and blasting parameter optimization
The data stored in the cloud are analyzed, processed and predicted by adopting a time sequence autoregressive moving average model, so that the correspondence between the rotating speed data and surrounding rock classification is realized, and according to different surrounding rock grades, different drilling and blasting parameters are adopted to optimize the drilling and blasting parameters;
s4, outputting and feeding back
Outputting the optimized drilling and blasting parameters to professionals, enabling staff to realize optimized adjustment of the drilling and blasting parameters according to cloud output results, feeding back the results to a site blasting related unit, realizing dynamic adjustment of the drilling and blasting parameters, and then applying the drilling and blasting parameters to subsequent drilling and blasting cycles to ensure drilling and blasting effects.
Preferably, the internet of things module is an important branch NB-IoT of the cellular-based narrowband internet of things which becomes the internet of everything; the NB-IoT is built in a cellular network, consumes only about 180KHz of bandwidth, and can be directly deployed in a GSM network, a UMTS network or an LTE network, so that deployment cost is reduced, and smooth upgrading is realized. NB-IOT modules are an emerging technology in the IOT field that supports cellular data connectivity of low power devices over a wide area network, also known as low power wide area network (LPWA). NB-IoT supports efficient connections for long standby times, high demand devices for network connections. NB-IoT device battery life can be increased to at least 10 years while still providing very comprehensive indoor cellular data connection coverage. NB-IOT modules focus on the low power wide coverage (LPWA) internet of things (IOT) market, an emerging technology that can be widely used worldwide. The method has the characteristics of wide coverage, multiple connections, low speed, low cost, low power consumption, good architecture and the like. The NB-IOT uses License frequency band, and can adopt three deployment modes of in-band, guard band or independent carrier waves and coexist with the existing network.
Preferably, in order to ensure the network transmission rate and the stability of data transmission, the Wireless Sensor Network (WSN) in the step S2 is a network formed by sensor nodes, which can monitor, sense and collect various information of the environment of the node deployment area or the sensing object of interest of the observer in real time, process the information and then send the information in a wireless manner, particularly, under the severe external environment where the common sensor technology equipment is difficult to apply, the detection nodes of the sensor are deployed around the sensing object to obtain information, and the information is transmitted to operators, so that the safety of the personnel is ensured, valuable information can be obtained, and the peripheral of the wireless sensor network distributes a plurality of sensors for sensing and checking the external world; the method specifically adopts the following data transmission algorithm to improve the transmission rate of the wireless sensor network and solve the problems of interference, data anomaly, conflict and congestion in the clustering routing process, and comprises the following steps: a channel allocation process of frequency random hopping, a detection, processing and release process of abnormal data, an inter-cluster contention transmission process based on collision avoidance, a congestion release process based on path transfer: wherein the method comprises the steps of
1. Channel allocation procedure for frequency random hopping
(1) During data transmission, nodes in the WSN are divided into a plurality of clusters, and a cluster head is responsible for data collection and calculation in the cluster and channel allocation process of the nodes in the cluster;
(2) All nodes have the same initial frequency, in a cluster collection stage, firstly, randomly selecting one frequency from a plurality of available frequencies to be distributed to a certain cluster head, then, secondarily distributing the frequency to all nodes in the cluster by the cluster head, and finally, completing the frequency distribution process of all nodes; the frequency formula allocated to the cluster head is:
f＝f 0 +cΔf
wherein: f (f) 0 Is the initial frequency of the node; c is a random number between (0, m-1), m is the number of available channels, and m is more than or equal to 0 and less than or equal to 16; Δf is a unit frequency step factor, where Δf=5 MHz;
2. abnormal data detection, processing and removal process
(1) Firstly, defining an abnormal data threshold, and judging the abnormal data when the threshold is met;
(2) Reporting the determined position and data set of the abnormal data to a cluster head, and forwarding the position and the data set to a base station by the cluster head, wherein only the abnormal data set is transmitted in a network, and all other nodes are kept dormant;
(3) If no abnormal data is detected, the network is restored to a state that all nodes normally transmit data;
3. inter-cluster contention transmission procedure based on collision avoidance
Firstly, defining a competition access function, wherein the larger the competition access function is, the higher the priority of cluster head node access is, the priority of cluster head node access is ordered by a receiving cluster head node, and when abnormal data exists in a data packet of the cluster head, the highest priority is given to the receiving cluster head, and the abnormal data packet is accessed and received;
4. congestion relief procedure based on path transfer
1. Defining a reserved queue length L j Judging network congestion by comparing queue length of access cluster head (i) with reserved queue length of receiving cluster head (j)In the case of L i <L j No congestion occurs; if L i >L j If congestion exists in the network, the congestion condition needs to be relieved.
2. And setting a flag bit CN, wherein when CN=0, the congestion-free state is indicated, when CN=1, the congestion condition is indicated, and the source cluster head node is informed of the congestion condition.
3. And finally, the cluster head executes a congestion relief process according to the announced congestion condition, processes the cluster head nodes in the congestion state, and relieves the congestion in the path.
(1) Firstly, congestion detection is carried out on an accessed cluster head node, and the congestion condition is judged;
(2) Then setting a flag bit, and informing a source cluster head node of the congestion condition;
(3) And finally, the cluster head node in the congestion state is processed, and the congestion in the path is relieved.
Preferably, the process of analyzing, processing and predicting the data stored in the cloud end by using the time series autoregressive moving average model in the step S3 includes
S31, carrying out data preprocessing on the data stored in the cloud
The data preprocessing comprises sequence stability test and white noise test, wherein the rotation speed data obtained in the earlier stage are stable non-white noise sequences except special cases, and if the obtained sequence does not meet one of the two sequences, the step is needed;
s32, carrying out differential operation on the non-stationary sequence to obtain a stationary non-white noise sequence
The subtraction between two sequence values one period apart is called 1-order difference operation, and so on, the order is generally taken as 1 or 2; the white noise data has no analysis value, so that white noise test is required, the data is normally non-white noise data, and when the data is white noise data, the analysis is stopped, and a stable non-white noise sequence is obtained through the process;
s33, after the stable non-white noise sequence is obtained, model order determination is carried out
The model is ordered to find optimal parameters p and q, and model parameters are determined:
wherein the formula of the autoregressive model (AR), the p-th order autoregressive model, is defined as:
wherein y is t With current value, μ being a constant term, γ i As autocorrelation coefficient epsilon t Is an error; in an autoregressive model (AR), the model-scaling process is the process of finding the optimal parameter p.
Wherein, the formula of the moving average Model (MA), the q-order moving average model is defined as:
wherein θ i Is a running average coefficient; in a moving average Model (MA), the model scaling procedure is a procedure to find the optimal parameter q.
S34, constructing a model
The formula for the autoregressive-moving average model (ARMA) is defined as:
the parameter meaning is the same as that described above, and in an autoregressive-moving average (ARMA) model, the model scaling process is a process of searching the optimal parameters p and q.
The model constructs an ARMA (p, q) model for the fixed-order parameters;
s35, utilizing the model to carry out future multiple time point results
The model prediction can predict a plurality of time point results in the future;
s36, evaluating results
And (3) evaluating the result, namely grading and corresponding the rotation speed prediction data and the surrounding rock, outputting the surrounding rock grade, for example, 100-150 r/min, as III-grade surrounding rock, and adopting different drilling and blasting parameters according to different surrounding rock grades to provide a drilling and blasting parameter optimization scheme.
Table 1: correspondence example table
| Surrounding rock grade | Rotating speed (r/min) |
| Ⅰ | 50r/min～80r/min |
| Ⅱ | 80r/min～100r/min |
| Ⅲ | 100r/min～150r/min |
| Ⅳ | 150r/min～210r/min |
| Ⅴ | 210r/min～250r/min |
The blasting parameter optimization takes the design of blasting parameters of different surrounding rock grades of a certain project as an example, the III-level surrounding rock is constructed by adopting a full-section method, and the IV-level and V-level surrounding rock is constructed by adopting a step method. When the tunnel surrounding rock grade is III, surrounding rock blasting parameters are as follows:
table 2: III level surrounding rock blasting parameter table
When the tunnel surrounding rock grade is grade IV, surrounding rock blasting parameters are shown in the following table 3:
table 3: IV-level surrounding rock blasting parameter table
Because the surrounding rock is a dynamic change process, the surrounding rock classification cannot be refined, if the surrounding rock classification is realized according to the rotating speed of the air drill rod every time, different surrounding rock grades adopt different levels of blasting parameters, parameters such as the diameter and depth of a blasthole, the placement of the blasthole, the drug loading and the like are optimized in time, the light explosion effect can be effectively ensured, the light explosion efficiency is improved, the dynamic adjustment of drilling and explosion parameters is realized, and the characteristics of good economic benefit and the like are achieved.
The embodiment provides a device for optimizing drilling and blasting parameters based on the rotation speed of an air drill rod, which is used for optimizing the drilling and blasting parameters based on the method, and comprises the following steps of
The battery supplies power for a device main board, and the device main board is an integrated board of the sensor module, the Internet of things module and the network optimization module;
the sensor modules are used for sensing and measuring the rotating speed of the air drill rod and are arranged on the Internet of things module;
the internet of things module is based on a cellular technology, is provided with the sensor and the network optimization module, and stores and transmits data acquired by the sensor;
and the network optimization module is internally used for uploading the rotating speed data to the cloud by using a data transmission algorithm for optimizing the link quality.
A system for optimizing drill burst parameters based on rotational speed of an air drill pipe, the system comprising a device end configured as means for performing a method of optimizing drill burst parameters based on rotational speed of an air drill pipe, a cloud end, and an output end.
Example 2: in contrast to embodiment 1, the process of using the time-series autoregressive moving average model in step S3 of embodiment 1 to analyze, process and predict the data stored in the cloud end to realize the optimization of drilling and blasting parameters is specifically as in this embodiment.
The implementation method 1 comprises the following steps: let us say that 50 sets of rotational speed data are obtained on a certain day:
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1. data preprocessing (best differential sequence diagram is shown in FIG. 9)
When the difference is 0 (i.e. the data does not need to be differential), the saliency P <0.05 appears to be significant horizontally, and the sequence is a smooth time sequence;
checking the model white noise according to the P value of the Q statistic (the P value is larger than 0.1 and is white noise); q6 (0.854 (0.355)) does not exhibit significance at the level, and cannot reject the assumption that the model residual is a white noise sequence, which is a non-white noise sequence;
2. after pretreatment, a stable non-white noise sequence is obtained
3. Model order determination
An Autocorrelation Chart (ACF) is shown in fig. 10, and a Partial Autocorrelation Chart (PACF) is shown in fig. 11;
determining p=0, q=0 according to fig. 10 and 11;
4. building a model
The final model was determined to be ARMA (0, 0), and the model formula was as follows: y (t) =102.68;
5. model prediction
Predicting that the five groups of results are 102.68;
6. evaluation of results
102.68r/min is III-grade surrounding rock within the range of 100 r/min-150 r/min, and III-grade surrounding rock blasting parameters are adopted; after blasting, the drilling and blasting parameters are corrected in time according to the analysis and comparison of blasting effects, so that the blasting effect is improved, and the technical and economic indexes are improved;
the implementation method 2 comprises the following steps: let us say that 50 sets of rotational speed data are obtained on a certain day:
(140，124，141，130，139，140，136，145，132，122，150，141，142，122，143，123，132，142，136，149，146，146，128，125，122，127，128，125，131，141，138，142，133，139，150，137，120，122，131，123，191，184，188，182，190，199，198，182，183，197)；
1. data preprocessing (best differential sequence diagram is shown in FIG. 12)
At the difference of 1 st order, salience P <0.05, appears salience on level, the sequence is a steady time sequence;
the model white noise is checked based on the P value of the Q statistic (P value is greater than 0.1 as white noise). Q6 (0.201 (0.654)) does not exhibit significance at the level, and cannot reject the assumption that the residual of the model is a white noise sequence, which is a non-white noise sequence.
2. A stable non-white noise sequence is obtained after pretreatment;
3. model order determination
An Autocorrelation Chart (ACF) is shown in fig. 13, and a Partial Autocorrelation Chart (PACF) is shown in fig. 14;
determining p and q parameters, and determining p=1 and q=0 according to the upper graph;
4. building a model
The final model was ARIMA (1, 0) and the model formula was as follows:
y(t)＝1.515-0.435*y(t-1)
5. model prediction
Five sets of results were predicted (196.34, 198.14, 198.87, 200.07, 201.06)
6. Evaluation of results
(196.34, 198.14, 198.87, 200.07, 201.06) r/min is in the range of 150-210 r/min, and is a class IV surrounding rock, and the blasting parameters of the class IV surrounding rock are adopted. After blasting, the drilling and blasting parameters are corrected in time according to the analysis and comparison of blasting effects, the blasting effect is improved, and the technical and economic indexes are improved.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A method for optimizing drilling and blasting parameters based on the rotating speed of an air drill rod is characterized by comprising the following steps: comprising
S1, measuring the rotation speed of the drill rod by using a sensor module
When drilling, attaching the sensor module to the drill rod of the pneumatic drill, and measuring the rotating speed data of the drill rod;
s2, uploading measured data to a cloud by using an Internet of things module and a network optimization module;
s3, in the cloud system, analyzing, processing and predicting stored data by using a time sequence autoregressive moving average model to realize drilling and blasting parameter optimization;
s4, outputting and feeding back
Outputting the optimized drilling and blasting parameters, and performing optimization adjustment on the drilling and blasting parameters according to the output result, so that the dynamic adjustment of the drilling and blasting parameters is realized, and the drilling and blasting parameters are applied to subsequent drilling and blasting cycles.
2. The method for optimizing drill burst parameters based on the rotational speed of the pneumatic drill rod as recited in claim 1, wherein: the internet of things module and the network optimization module described in the step S2 are integrated in the same circuit;
the internet of things module is used for sensing and collecting drill rod data and uploading drill rod rotating speed data to the internet cloud for storage;
the sensor comprises a rotation speed sensor and a sensor required by a network optimization module;
and the network optimization module is used for uploading the rotating speed data to the cloud end through wireless sensor network transmission by using a data transmission algorithm for optimizing the link quality.
3. The method for optimizing drill burst parameters based on the rotational speed of the pneumatic drill rod as recited in claim 2, wherein: the wireless sensor network is a network formed by sensor nodes, a plurality of sensors for sensing and checking the external world in real time are distributed at the terminal of the wireless sensor network, and the processing process comprises the following steps of
1. Channel allocation of frequency random jump of the wireless sensor network is carried out, and the transmission rate of the wireless sensor network is improved;
2. detecting, processing and relieving the abnormal data to finish the detection of the abnormal data;
3. performing inter-cluster competitive transmission based on collision avoidance on abnormal data, and solving the interference of a clustering routing process;
4. congestion relief based on path transfer solves the congestion problem in the clustering routing process.
4. A method of optimizing drill burst parameters based on rotational speed of an air drill pipe as recited in claim 3, wherein: the process of channel allocation for frequency random hopping of a wireless sensor network includes
(1) During data transmission, dividing nodes in the WSN into a plurality of clusters, wherein a cluster head is responsible for data collection and calculation in the clusters and channel allocation process of the nodes in the clusters;
(2) All nodes have the same initial frequency, in a cluster collection stage, firstly, randomly selecting one frequency from a plurality of available frequencies to be distributed to a certain cluster head, then, secondarily distributing the frequency to all nodes in the cluster by the cluster head, and finally, completing the frequency distribution process of all nodes; the frequency formula allocated to the cluster head is:
f＝f 0 +cΔf
wherein: f (f) 0 Is the initial frequency of the node; c is a random number between (0, m-1), m is the number of available channels, and m is more than or equal to 0 and less than or equal to 16; Δf is a unit frequency step factor.
5. A method of optimizing drill burst parameters based on rotational speed of an air drill pipe as recited in claim 3, wherein: the process of detecting, processing and relieving abnormal data comprises
(1) Firstly, defining an abnormal data threshold, and judging the abnormal data when the threshold is met;
(2) Reporting the determined position and data set of the abnormal data to a cluster head, and forwarding the position and the data set to a base station by the cluster head, wherein only the abnormal data set is transmitted in a network, and all other nodes are kept dormant;
(3) If no abnormal data is detected, the network is restored to the state that all nodes normally transmit data.
6. A method of optimizing drill burst parameters based on rotational speed of an air drill pipe as recited in claim 3, wherein: the process of inter-cluster contention transmission based on collision avoidance and congestion relief based on path transfer for abnormal data includes
Inter-cluster contention transmission procedure based on collision avoidance
Defining a competition access function, wherein the larger the competition access function is, the higher the priority of cluster head node access is, the priority of cluster head node access is sequenced by a receiving cluster head node, and when abnormal data exists in a data packet of the cluster head, the highest priority is given to the receiving cluster head, and the abnormal data packet is accessed and received;
congestion relief procedure based on path transfer
(1) Firstly, congestion detection is carried out on an accessed cluster head node, and the congestion condition is judged;
(2) Then setting a flag bit, and informing a source cluster head node of the congestion condition;
(3) And finally, the cluster head node in the congestion state is processed, and the congestion in the path is relieved.
7. The method for optimizing drill burst parameters based on the rotational speed of the pneumatic drill rod as recited in claim 1, wherein: the process of using the time series autoregressive moving average model to analyze, process and predict the data stored in the cloud end in the step S3 to realize the optimization of drilling and blasting parameters comprises the following steps of
S31, carrying out data preprocessing on the data stored in the cloud
The method comprises the steps of checking sequence stability and white noise, and if the obtained sequence does not meet one of the two, performing step S32;
s32, carrying out differential operation on the non-stationary sequence to obtain a stationary non-white noise sequence;
s33, after the stable non-white noise sequence is obtained, model scaling is carried out;
s34, constructing a model
The definition of the autoregressive-moving average model (ARMA) is:
the model constructs an ARMA (p, q) model for the fixed-order parameters;
s35, utilizing the model to carry out a plurality of time point results in the future;
s36, evaluating results
And (5) the rotation speed prediction data are corresponding to the surrounding rock grades, and the surrounding rock grades are output.
8. The method for optimizing drill burst parameters based on the rotational speed of the pneumatic drill pipe as recited in claim 7, wherein: the model scaling process of step S33 includes
According to the autoregressive model (AR), the definition of the p-th order autoregressive model is:
wherein y is t With current value, μ being a constant term, γ i As autocorrelation coefficient epsilon t Is an error
According to the moving average Model (MA), the definition of the q-order moving average model is:
wherein θ i Is a coefficient of sliding average
And searching optimal parameters p and q, and determining model parameters.
9. An apparatus based on the method of claim 1, wherein: comprising
The battery supplies power for a device main board, and the device main board is an integrated board of the sensor module, the Internet of things module and the network optimization module;
the sensor modules are used for sensing and measuring the rotating speed of the air drill rod and are arranged on the Internet of things module;
the internet of things module is based on a cellular technology, is provided with the sensor and the network optimization module, and stores and transmits data acquired by the sensor;
and the network optimization module is internally used for uploading the rotating speed data to the cloud by using a data transmission algorithm for optimizing the link quality.
10. A system for optimizing drilling and blasting parameters based on the rotating speed of an air drill rod is characterized in that: the system comprises a device side, a cloud end and an output side, the device side being configured as means for performing the method of any of claims 1-8.
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