A method for optimizing drilling and blasting parameters based on the rotation speed of the pneumatic drill rod

Abstract

The invention discloses a method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod, which belongs to the technical field of construction blasting, and comprises step S1. using a sensor module to measure the rotation speed of the drill rod, step S2. using an Internet of Things module and a network optimization module to upload the measured data to the cloud; step S3. using a time series autoregressive moving average model to analyze, process and predict stored data, so as to optimize the drilling and blasting parameters; step S4. output and feedback; the method realizes the optimization of drilling and blasting parameters by collecting pneumatic drill drilling data in real time, using a time series autoregressive moving average model to analyze, process and predict data, staff adjusts the drilling and blasting parameters according to the output results, and finally feeds back the results to the site, so as to realize the dynamic adjustment of the drilling and blasting parameters; the method can effectively ensure the light blasting effect and improve the light blasting efficiency, and has the characteristics of good parameter adjustment processing effect, being able to effectively ensure the light blasting effect, realizing the dynamic adjustment of the drilling and blasting parameters, achieving good economic benefits, and the like.

Description

Technical Field

[0001] The invention relates to the technical field of construction blasting, and in particular to a method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod. Background Art

[0002] A pneumatic rock drill, also known as a pneumatic drill, is a percussive drilling machine powered by compressed air. Pneumatic drilling and blasting is widely used in mountain tunnel construction. It boasts a wide range of applications, low purchase and operating costs, and relatively easy control of overexcavation. Overall, pneumatic drills remain the mainstream rock drilling equipment for smooth blasting in tunnels in China.

[0003] Currently, pneumatic drills only drill according to the parameters designed for existing blasting schemes and cannot continuously optimize blasting parameters. However, this device uses a device to assist in measuring the drill rod speed. This information is transmitted via an Internet of Things (IoT) module, and then, within a network optimization module, a data transmission algorithm that optimizes link quality is used to upload the speed data to the cloud. The cloud system stores the drill rod speed data from different sources and analyzes and predicts it using a time series autoregressive moving average model. The main steps are as follows: First, preprocess the previously collected data, determine the model order for the stationary non-white noise sequence, construct a model, and then perform predictions to match the speed data with the surrounding rock classification. Based on the different surrounding rock classifications, different drilling and blasting parameters are used to propose optimized drilling and blasting parameter plans. Staff evaluate the cloud-based output, enabling dynamic adjustment of the drilling and blasting parameters to achieve better economic benefits. The drilling and blasting parameters can be adjusted after each pneumatic drill to achieve a better light blasting effect. This invention proposes a method, device, and system for optimizing drilling and blasting parameters based on the speed of the pneumatic drill rod, which is of great significance for optimizing drilling and blasting parameters in tunnel construction.

[0004] In view of the above problems, it is urgent to design a method for optimizing drilling and blasting parameters based on the rotation speed of the pneumatic drill rod to solve the problems existing in the above-mentioned prior art. Summary of the Invention

[0005] In response to the above-mentioned problems, the present invention aims to provide a method for optimizing drilling and blasting parameters based on the rotational speed of a pneumatic drill rod. This method optimizes the drilling and blasting parameters by adjusting the drilling and blasting parameters after each real-time use of the pneumatic drill to analyze, process and predict the parameters using a time series autoregressive moving average model in a cloud system. The method then provides feedback to on-site professionals, who optimize and adjust the drilling and blasting parameters based on the cloud output results. Finally, the results are fed back to on-site blasting-related units to achieve dynamic adjustment of the drilling and blasting parameters, which are then applied to subsequent drilling and blasting cycles. The method can effectively ensure the light blasting effect and improve the light blasting efficiency, and has the characteristics of good parameter adjustment processing effect, effective guarantee of the light blasting effect, dynamic adjustment of the drilling and blasting parameters, and better economic benefits.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod, comprising:

[0008] Step S1. Use the sensor module to measure the drill rod speed

[0009] When drilling, attach the sensor module to the drill rod of the pneumatic drill to measure the rotation speed data of the drill rod;

[0010] Step S2. Use the Internet of Things module and the network optimization module to upload the measured data to the cloud;

[0011] Step S3. In the cloud system, use the time series autoregressive moving average model to analyze, process, and predict the stored data to optimize drilling and blasting parameters;

[0012] Step S4. Output and feedback

[0013] Output the optimized drilling and blasting parameters, optimize and adjust the drilling and blasting parameters according to the output results, realize dynamic adjustment of drilling and blasting parameters, and apply them to subsequent drilling and blasting cycles.

[0014] Preferably, the Internet of Things module and the network optimization module described in step S2 are integrated into the same circuit;

[0015] The IoT module is used to sense and collect drill pipe data, and then upload the drill pipe speed data to the Internet cloud storage;

[0016] Wherein, the sensors include speed sensors and sensors required by the network optimization module;

[0017] The network optimization module is used to upload the speed data to the cloud via the wireless sensor network using a data transmission algorithm that optimizes the link quality.

[0018] Preferably, the wireless sensor network transmission uses a wireless sensor network for data transmission. The wireless sensor network is a network composed of sensor nodes, and a plurality of sensors that perceive and check the outside world in real time are distributed at the ends of the wireless sensor network. The processing process includes

[0019] 1. Perform frequency random hopping channel allocation for wireless sensor networks to improve the transmission rate of wireless sensor networks;

[0020] 2. Detect, process and resolve abnormal data to complete the detection of abnormal data;

[0021] 3. Perform inter-cluster contention transmission based on conflict avoidance for abnormal data to resolve interference in the cluster routing process;

[0022] 4. Congestion relief based on path transfer solves the congestion problem in the cluster routing process.

[0023] Preferably, the process of channel allocation for random frequency hopping of wireless sensor networks includes:

[0024] (1) During data transmission, the nodes in the WSN are divided into multiple clusters. The cluster head is responsible for data collection, calculation, and channel allocation within the cluster.

[0025] (2) All nodes have the same initial frequency. In the cluster collection phase, a frequency is first randomly selected from multiple available frequencies and assigned to a cluster head. Then, the cluster head redistributes the frequency to all nodes in the cluster, and finally completes the frequency allocation process for all nodes. The frequency allocation formula for the cluster head is:

[0026] f=f0+cΔf

[0027] Where: f0 is the initial frequency of the node; c is a random number between (0, m-1), m is the number of available channels, 0≤m≤16; Δf is the unit frequency step factor.

[0028] Preferably, the process of detecting, processing and resolving abnormal data includes

[0029] (1) First, define the abnormal data threshold. When the threshold is met, it is determined to be abnormal data.

[0030] (2) The location and data set of the determined abnormal data are reported to the cluster head, which then forwards it to the base station. Only the abnormal data set is transmitted in the network, while all other nodes remain dormant.

[0031] (3) If no abnormal data is detected, the network returns to a state where all nodes transmit data normally.

[0032] Preferably, the process of performing inter-cluster contention transmission based on conflict avoidance and congestion relief based on path transfer for abnormal data includes:

[0033] Inter-cluster contention transmission process based on collision avoidance

[0034] Define a contention access function. The larger the contention access function, the higher the priority of the cluster head node. The receiving cluster head node ranks the priority of the access cluster head node. When there is abnormal data in the data packet of the cluster head, the receiving cluster head gives the highest priority to the abnormal data packet that is accessed and received.

[0035] Congestion relief process based on path transfer

[0036] (1) First, perform congestion detection on the connected cluster head node to determine the congestion situation;

[0037] (2) Then set the flag bit to inform the source cluster head node of the congestion situation;

[0038] (3) Finally, the cluster head node in the congested state is processed to relieve the congestion in the path.

[0039] Preferably, the process of using the time series autoregressive moving average model to analyze, process and predict the data stored in the cloud to optimize the drilling and blasting parameters in step S3 includes:

[0040] Step S31. Preprocess the data stored in the cloud

[0041] Including sequence stationarity test and white noise test. If the obtained sequence does not meet either of the two, proceed to step S32;

[0042] Step S32. Performing a differential operation on the non-stationary sequence to obtain a stationary non-white noise sequence;

[0043] Step S33. After obtaining the above-mentioned stationary non-white noise sequence, perform model order determination;

[0044] Step S34. Build model

[0045] The autoregressive moving average model (ARMA) is defined as:

[0046]

[0047] The model is an ARMA (p, q) model constructed by the above-mentioned fixed-order parameters;

[0048] Step S35. Utilize the above model to calculate the results at multiple future time points;

[0049] Step S36. Evaluate the results

[0050] Match the speed prediction data with the surrounding rock classification and output the surrounding rock classification.

[0051] Preferably, the model order determination process described in step S33 includes

[0052] According to the autoregressive model (AR), the p-order autoregressive model is defined as:

[0053]

[0054] Among them, y t is the current value, μ is the constant term, γ i is the autocorrelation coefficient, ε t Error

[0055] According to the moving average model (MA), the q-order moving average model is defined as:

[0056]

[0057] Among them, θ i is the sliding average coefficient

[0058] Find the optimal parameters p and q and determine the model parameters.

[0059] The beneficial effects of the present invention are as follows: the present invention discloses a method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod. Compared with the prior art, the present invention has the following improvements:

[0060] 1. The present invention designs a method for optimizing drilling and blasting parameters based on the rotational speed of a pneumatic drill pipe, comprising the steps of measuring the rotational speed of the drill pipe using a sensor module, uploading the measured data to a cloud system using an Internet of Things module and a network optimization module, and analyzing, processing, and predicting the data stored in the cloud system using a time series autoregressive moving average model to optimize, output, and provide feedback on the drilling and blasting parameters. The results are fed back to on-site blasting units to achieve dynamic adjustment of the drilling and blasting parameters, which are then applied in subsequent drilling and blasting cycles to ensure the drilling and blasting effect. This method can effectively ensure the smooth blasting effect and improve the smooth blasting efficiency.

[0061] 2. The present invention designs a specific process of using a time series autoregressive moving average model to analyze, process and predict data stored in the cloud. First, the data collected in the early stage is preprocessed, the model order is determined for the stationary non-white noise sequence, and the model is constructed. Then the model is predicted to achieve the correspondence between the rotational speed data and the surrounding rock classification. Different drilling and blasting parameters are used according to different surrounding rock grades, and a drilling and blasting parameter optimization scheme is proposed; the staff evaluates the cloud output results to achieve the correspondence between the rotational speed data and the surrounding rock classification. Different drilling and blasting parameters are used according to different surrounding rock grades, and a drilling and blasting parameter optimization scheme is proposed; the staff optimizes and adjusts the drilling and blasting parameters according to the cloud output results, and finally feeds back the results to the on-site blasting unit, which can realize dynamic adjustment of the drilling and blasting parameters and achieve better economic benefits. It has the advantages of good parameter adjustment processing effect, effective guarantee of light blasting effect, and dynamic adjustment of drilling and blasting parameters to achieve better economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is an algorithm flow chart of a method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod.

[0063] Figure 2 This is a workflow diagram of the wireless sensor network of the present invention.

[0064] Figure 3 The figure is a flow chart of the channel allocation process of random frequency hopping according to the present invention.

[0065] Figure 4The figure is a flow chart of the abnormal data detection, processing and resolution process of the present invention.

[0066] Figure 5 The figure is a flow chart of the inter-cluster contention transmission process based on conflict avoidance of the present invention.

[0067] Figure 6 This is a flow chart of the congestion relief process based on path transfer of the present invention.

[0068] Figure 7 This is a structural block diagram of a device for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod according to the present invention.

[0069] Figure 8 This is a flow chart of the algorithm for analyzing, processing and predicting data using the time series autoregressive moving average model of the present invention.

[0070] Figure 9 This is the optimal differential sequence diagram I of Example 2 of the present invention.

[0071] Figure 10 This is the autocorrelation diagram (ACF) I of Example 2 of the present invention.

[0072] Figure 11 This is the partial autocorrelation diagram (PACF) I of Example 2 of the present invention.

[0073] Figure 12 This is the optimal differential sequence diagram II of Example 2 of the present invention.

[0074] Figure 13 This is the autocorrelation diagram (ACF) II of Example 2 of the present invention.

[0075] Figure 14 This is the partial autocorrelation diagram (PACF) II of Example 2 of the present invention. DETAILED DESCRIPTION

[0076] 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 in conjunction with the accompanying drawings and embodiments.

[0077] Example 1: Refer to the attached Figure 1-14The method shown is a method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod. The design idea of ​​this method is to use a device to assist in measuring the rotation speed of the drill rod each time a hole is drilled; the obtained drill rod rotation speed information is transmitted through the Internet of Things module, and the data transmission algorithm for optimizing the link quality is used in the network optimization module to upload the rotation speed data to the cloud; for the data stored in the cloud, the cloud system stores the drill rod rotation speeds from different sources and uses the time series autoregressive moving average model (ARIMA) for analysis, processing and prediction to achieve the correspondence between the rotation speed data and the surrounding rock classification, and adopts different drilling and blasting parameters according to different surrounding rock grades, and proposes a drilling and blasting parameter optimization plan; the staff optimizes and adjusts the drilling and blasting parameters according to the cloud output results, and finally feeds back the results to the on-site blasting unit to achieve dynamic adjustment of the drilling and blasting parameters and guide subsequent drilling and blasting processes; the specific process includes

[0078] Step S1. Use the sensor module to measure the drill rod speed

[0079] During each drilling, the sensor module is attached to the drill rod of the pneumatic drill to measure the rotation speed data of the drill rod and obtain a rotation speed data set of the drill rod. The sensor module is a plurality of rotation speed sensors.

[0080] Step S2. Use the Internet of Things module and the network optimization module to upload the measured data to the cloud. The Internet of Things module and the network optimization module are integrated in the same circuit;

[0081] Multiple sensors are distributed on the Internet of Things module to sense and collect data, and then upload the drill pipe speed data to the Internet cloud storage. The sensors include speed sensors and sensors required by the network optimization module;

[0082] The network optimization module uses a data transmission algorithm that optimizes link quality to improve the transmission rate and data transmission stability of the wireless sensor network and uploads the speed data to the cloud;

[0083] Step S3. In the cloud system, use the time series autoregressive moving average model to analyze, process and predict the data stored in the cloud to achieve drilling and blasting parameter optimization

[0084] The data stored in the cloud is analyzed, processed and predicted using a time series autoregressive moving average model to achieve a correspondence between rotational speed data and surrounding rock classification. Different drilling and blasting parameters are used according to different surrounding rock grades to optimize the drilling and blasting parameters.

[0085] Step S4. Output and feedback

[0086] The optimized drilling and blasting parameters are output to professionals, who optimize and adjust the drilling and blasting parameters based on the cloud output results. Finally, the results are fed back to the on-site blasting-related units to achieve dynamic adjustment of the drilling and blasting parameters, which are then applied to subsequent drilling and blasting cycles to ensure the drilling and blasting effect.

[0087] Preferably, the IoT module is NB-IoT, a cellular-based narrowband IoT (NB-IoT) network, a key component of the Internet of Everything (IoT). Built on cellular networks, NB-IoT consumes only approximately 180 kHz of bandwidth and can be directly deployed on GSM, UMTS, or LTE networks, reducing deployment costs and enabling smooth upgrades. The NB-IoT module is an emerging technology in the IoT field, supporting cellular data connections for low-power devices over wide-area networks (WANs), also known as Low Power Wide Area (LPWA). NB-IoT supports efficient connectivity for devices with long standby times and demanding network connectivity. NB-IoT device battery life can be increased to at least 10 years, while also providing comprehensive indoor cellular data coverage. Targeting the Low Power Wide Area (LPWA) IoT market, the NB-IoT module is an emerging technology with global potential. It offers wide coverage, multiple connections, low speeds, low cost, low power consumption, and an optimized architecture. NB-IoT uses licensed frequency bands and can be deployed in three modes: in-band, guard band, or as a standalone carrier, allowing it to coexist with existing networks.

[0088] Preferably, in order to ensure the network transmission rate and the stability of data transmission, the wireless sensor network (WSN) described in step S2 is designed to be a network composed of sensor nodes, which can monitor, sense and collect various information of the environment of the node deployment area or the sensed object of interest to the observer in real time, and process this information and send it out wirelessly. In particular, in harsh external environments where ordinary sensor technology equipment is difficult to apply, the sensor detection nodes are deployed around the sensed object to obtain information and transmit the information to the operator, which not only ensures the safety of the personnel but also obtains valuable information. The ends of the wireless sensor network are distributed with multiple sensors that sense and inspect the outside world; it specifically adopts the following data transmission algorithm to improve the transmission rate of the wireless sensor network and solve the interference, data anomaly, conflict and congestion problems in the cluster routing process, including: a channel allocation process with random frequency hopping, a process of detecting, processing and releasing abnormal data, an inter-cluster competition transmission process based on conflict avoidance, and a congestion relief process based on path transfer: wherein

[0089] 1. Channel allocation process with random frequency hopping

[0090] (1) During data transmission, the nodes in the WSN are divided into multiple clusters. The cluster head is responsible for data collection, calculation, and channel allocation within the cluster.

[0091] (2) All nodes have the same initial frequency. In the cluster collection phase, a frequency is first randomly selected from multiple available frequencies and assigned to a cluster head. Then, the cluster head redistributes the frequency to all nodes in the cluster, and finally completes the frequency allocation process for all nodes. The frequency allocation formula for the cluster head is:

[0092] f=f0+cΔf

[0093] Where: f0 is the initial frequency of the node; c is a random number between (0, m-1), m is the number of available channels, 0≤m≤16; Δf is the unit frequency step factor, where Δf=5MHz;

[0094] 2. Abnormal data detection, processing and resolution process

[0095] (1) First, define the abnormal data threshold. When the threshold is met, it is determined to be abnormal data.

[0096] (2) The location and data set of the determined abnormal data are reported to the cluster head, which then forwards it to the base station. Only the abnormal data set is transmitted in the network, while all other nodes remain dormant.

[0097] (3) If no abnormal data is detected, the network returns to a state where all nodes transmit data normally;

[0098] 3. Inter-cluster contention transmission process based on conflict avoidance

[0099] First, a contention access function is defined. The larger the contention access function is, the higher the priority of the cluster head node is. The receiving cluster head node ranks the priority of the access cluster head node. When there is abnormal data in the data packet of the cluster head, the receiving cluster head is given the highest priority and accesses and receives the abnormal data packet.

[0100] 4. Congestion relief process based on path transfer

[0101] 1. Define the reserved queue length L j , the network congestion is judged by comparing the queue length of the access cluster head (i) and the reserved queue length of the receiving cluster head (j). If L i <L j , then there is no congestion; if L i >L j , it is considered that there is congestion in the network and the congestion needs to be relieved.

[0102] 2. Set the flag CN. When CN=0, it indicates no congestion. When CN=1, it indicates congestion. The congestion situation is notified to the source cluster head node.

[0103] 3. Finally, the cluster head executes the congestion relief process according to the notified congestion situation, handles the cluster head node in the congested state, and relieves the congestion in the path.

[0104] (1) First, perform congestion detection on the connected cluster head node to determine the congestion situation;

[0105] (2) Then set the flag bit to inform the source cluster head node of the congestion situation;

[0106] (3) Finally, the cluster head node in the congested state is processed to relieve the congestion in the path.

[0107] Preferably, the process of using the time series autoregressive moving average model to analyze, process and predict the data stored in the cloud to optimize the drilling and blasting parameters in step S3 includes:

[0108] Step S31. Preprocess the data stored in the cloud

[0109] The data preprocessing includes sequence stationarity test and white noise test. Except for special cases, the speed data obtained in the early stage are all stationary non-white noise sequences. If the obtained sequence does not meet either of the two requirements, this step must be performed.

[0110] Step S32. Perform differential operation on the non-stationary sequence to obtain a stationary non-white noise sequence

[0111] The subtraction operation between two sequence values ​​that are one period apart is called a first-order difference operation. Similarly, the order is generally 1 or 2. White noise data has no analytical value, so a white noise test is required. In general, the data is non-white noise data. When it is white noise data, the analysis is stopped. The above process can be used to obtain a stationary non-white noise sequence.

[0112] Step S33. After obtaining the above-mentioned stationary non-white noise sequence, perform model order determination

[0113] The model order determination is to find the optimal parameters p and q and determine the model parameters:

[0114] The autoregressive model (AR) and the formula of the p-order autoregressive model are defined as:

[0115]

[0116] Among them, y t is the current value, μ is the constant term, γ i is the autocorrelation coefficient, ε t is the error; in the autoregressive model (AR), the model order determination process is the process of finding the optimal parameter p.

[0117] Among them, the moving average model (MA), the formula of the q-order moving average model is defined as:

[0118]

[0119] Among them, θ i is the sliding average coefficient; in the sliding average model (MA), the model order determination process is the process of finding the optimal parameter q.

[0120] Step S34. Build model

[0121] The formula of the Autoregressive Moving Average Model (ARMA) is defined as:

[0122]

[0123] The meanings of the parameters are the same as described above. In the autoregressive moving average model (ARMA) model, the model order determination process is the process of finding the optimal parameters p and q.

[0124] The model is an ARMA (p, q) model constructed by the above-mentioned fixed-order parameters;

[0125] Step S35. Use the above model to calculate the results at multiple time points in the future

[0126] The model prediction can predict the results at multiple time points in the future;

[0127] Step S36. Evaluate the results

[0128] The result evaluation matches the speed prediction data with the surrounding rock classification and outputs the surrounding rock grade. For example, 100r / min to 150r / min represents Grade III surrounding rock. Different drilling and blasting parameters are used according to different surrounding rock grades, and a drilling and blasting parameter optimization plan is proposed.

[0129] Table 1: Correspondence table

[0130] Surrounding rock grade 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

[0131] Taking the blasting parameter design of different surrounding rock grades of a certain project as an example, the full section method is used for construction of grade III surrounding rock, and the step method is used for construction of grade IV and V surrounding rock. When the tunnel surrounding rock grade is grade III, the surrounding rock blasting parameters are as follows:

[0132] Table 2: Level III surrounding rock blasting parameters

[0133]

[0134] When the tunnel surrounding rock grade is IV, the surrounding rock blasting parameters are as shown in Table 3:

[0135] Table 3: Level IV surrounding rock blasting parameters

[0136]

[0137] Since the surrounding rock is a dynamic process, it is impossible to achieve fine-grained surrounding rock classification. If we can achieve fine-grained surrounding rock classification based on the rotation speed of the air drill drill rod each time, use different levels of blasting parameters for different surrounding rock grades, and timely optimize parameters such as blasthole diameter and depth, blasthole arrangement, and charge amount, we can effectively ensure the light blasting effect, improve the light blasting efficiency, realize dynamic adjustment of drilling and blasting parameters, and achieve good economic benefits.

[0138] Example 2: Different from the above-mentioned Example 1, the process of using the time series autoregressive moving average model to analyze, process and predict the data stored in the cloud in step S3 of Example 1 to achieve drilling and blasting parameter optimization is specifically as shown in this example.

[0139] Implementation method 1: Assume that 50 sets of speed data are obtained on a certain day:

[0140] (98, 119, 104, 119, 93, 98, 94, 100, 92, 119, 103, 109, 100, 94, 112, 112, 105, 103, 95, 101, 103, 96, 103, 102, 96, 90, 102, 98, 102, 108, 91, 106, 107, 100, 110, 94, 115, 102, 106, 113, 102, 91, 104, 108, 104, 101, 94, 104, 110, 102)

[0141] 1. Data preprocessing (optimal difference sequence diagram such as Figure 9 shown)

[0142] When the difference is 0 order (that is, the data does not need to be differentiated), the significance level is P<0.05, and the sequence is a stationary time series;

[0143] The model white noise was tested based on the P value of the Q statistic (P value greater than 0.1 indicates white noise); Q6 (0.854 (0.355)) was not significant at the level, and the hypothesis that the residual of the model is a white noise sequence cannot be rejected. The sequence is a non-white noise sequence;

[0144] 2. After preprocessing, a stationary non-white noise sequence is obtained

[0145] 3. Model order determination

[0146] Autocorrelation plot (ACF) Figure 10 As shown, the partial autocorrelation diagram (PACF) is as follows Figure 11 As shown;

[0147] according to Figure 10 and Figure 11 Determine p=0, q=0;

[0148] 4. Build the model

[0149] The final model is determined to be ARMA (0, 0), and the model formula is as follows: y (t) = 102.68;

[0150] 5. Model Prediction

[0151] The predicted results for all five groups were 102.68;

[0152] 6. Results Evaluation

[0153] 102.68r / min is in the range of 100r / min to 150r / min, which is Class III surrounding rock. Use Class III surrounding rock blasting parameters. After blasting, analyze and compare the blasting effects and promptly revise the drilling and blasting parameters to improve the blasting effect and technical and economic indicators.

[0154] Implementation method 2: Assume that 50 sets of speed data are obtained on a certain day:

[0155] (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);

[0156] 1. Data preprocessing (optimal difference sequence diagram such as Figure 12 shown)

[0157] When the difference is 1st order, the significance level is P<0.05, and the sequence is a stationary time series.

[0158] The model is tested for white noise based on the P value of the Q statistic (a P value greater than 0.1 indicates white noise). Q6 (0.201(0.654)) is not significant at the level, and the hypothesis that the model residual is a white noise sequence cannot be rejected. The sequence is non-white noise.

[0159] 2. After preprocessing, a stationary non-white noise sequence is obtained;

[0160] 3. Model order determination

[0161] Autocorrelation plot (ACF) Figure 13 As shown, the partial autocorrelation diagram (PACF) is as follows Figure 14 As shown;

[0162] Determine the p and q parameters. According to the figure above, determine p = 1 and q = 0.

[0163] 4. Build the model

[0164] The final model is determined to be ARIMA (1, 1, 0), and the model formula is as follows:

[0165] y(t)=1.515-0.435*y(t-1)

[0166] 5. Model Prediction

[0167] The predicted five groups of results are (196.34, 198.14, 198.87, 200.07, 201.06)

[0168] 6. Results Evaluation

[0169] (196.34, 198.14, 198.87, 200.07, 201.06) r / min is within the range of 150 r / min to 210 r / min, indicating Class IV surrounding rock. Class IV surrounding rock blasting parameters should be used. After blasting, the drilling and blasting parameters should be promptly revised based on blasting effect analysis and comparison to improve blasting results and technical and economic indicators.

[0170] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod, characterized in that: include Step S1. Use the sensor module to measure the drill pipe speed When drilling, attach the sensor module to the drill rod of the pneumatic drill to measure the rotation speed data of the drill rod; Step S2. Use the Internet of Things module and the network optimization module to upload the measured data to the cloud; Step S3. In the cloud system, use the time series autoregressive moving average model to analyze, process and predict the stored data to optimize the drilling and blasting parameters; Step S4. Output and feedback Output optimized drilling and blasting parameters, optimize and adjust the drilling and blasting parameters according to the output results, realize dynamic adjustment of drilling and blasting parameters, and apply them to subsequent drilling and blasting cycles; The process of using the time series autoregressive moving average model to analyze, process and predict the data stored in the cloud to optimize the drilling and blasting parameters in step S3 includes: Step S31: Preprocess the data stored in the cloud Including sequence stationarity test and white noise test. If the obtained sequence does not meet either of the two, proceed to step S32; Step S32. Performing a differential operation on the non-stationary sequence to obtain a stationary non-white noise sequence; Step S33. After obtaining the above-mentioned stationary non-white noise sequence, determine the model order; Step S34. Build model The autoregressive moving average model (ARMA) is defined as: The model is an ARMA (p, q) model constructed by the above-mentioned fixed-order parameters; Step S35. Utilize the above model to perform results at multiple future time points; Step S36. Evaluate the results The speed prediction data is matched with the surrounding rock classification, and the surrounding rock grade is output. Different drilling and blasting parameters are adopted according to different surrounding rock grades, and the drilling and blasting parameter optimization scheme is proposed: Among them, the full section method is used for construction of grade III surrounding rock, and the step method is used for construction of grade IV and V surrounding rock; The model order determination process described in step S33 includes: According to the autoregressive model (AR), the p-order autoregressive model is defined as: Among them, y t is the current value, μ is a constant term, γ i is the autocorrelation coefficient, ε t Error According to the moving average model (MA), the q-order moving average model is defined as: Among them, θ i is the sliding average coefficient Find the optimal parameters p and q and determine the model parameters.

2. A method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod according to claim 1, characterized in that: The Internet of Things module and the network optimization module described in step S2 are integrated into the same circuit; The IoT module is used to sense and collect drill pipe data, and then upload the drill pipe speed data to the Internet cloud storage; Wherein, the sensors include rotation speed sensors and sensors required by the network optimization module; The network optimization module is used to upload the rotation speed data to the cloud through the wireless sensor network using a data transmission algorithm that optimizes the link quality.

3. A method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod according to claim 2, characterized in that: The wireless sensor network transmission uses a wireless sensor network for data transmission. The wireless sensor network is a network composed of sensor nodes, and multiple sensors that perceive and check the outside world in real time are distributed at the end of the wireless sensor network. The processing process includes (1) Perform frequency random hopping channel allocation for wireless sensor networks to improve the transmission rate of wireless sensor networks; (2) Detect, process and resolve abnormal data to complete the detection of abnormal data; (3) Perform inter-cluster contention transmission based on conflict avoidance for abnormal data to resolve interference in the cluster routing process; (4) Congestion relief based on path transfer can solve the congestion problem in the cluster routing process.

4. A method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod according to claim 3, characterized in that: The process of channel allocation for random frequency hopping in wireless sensor networks includes: (1) During data transmission, the nodes in the WSN are divided into multiple clusters. The cluster head is responsible for data collection, calculation, and channel allocation within the cluster. (2) All nodes have the same initial frequency. In the cluster collection phase, a frequency is first randomly selected from multiple available frequencies and assigned to a cluster head. Then, the cluster head redistributes the frequency to all nodes in the cluster, and finally completes the frequency allocation process for all nodes. The frequency formula for the cluster head is: f=f0+cΔf Where: f0 is the initial frequency of the node; c is a random number between (0, m-1), m is the number of available channels, 0≤m≤16; Δf is the unit frequency step factor.

5. The method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod according to claim 3, characterized in that: The process of detecting, processing and resolving abnormal data includes (1) First, define the abnormal data threshold. When the threshold is met, it is determined to be abnormal data. (2) The location and data set of the determined abnormal data are reported to the cluster head, which then forwards it to the base station. Only the abnormal data set is transmitted in the network, while all other nodes remain dormant. (3) If no abnormal data is detected, the network returns to a state where all nodes transmit data normally.

6. A method for optimizing drilling and blasting parameters based on the rotation speed of a pneumatic drill rod according to claim 3, characterized in that: The process of inter-cluster contention transmission based on conflict avoidance and congestion relief based on path transfer for abnormal data includes: Inter-cluster contention transmission process based on conflict avoidance Define a competitive access function. The larger the competitive access function is, the higher the priority of the cluster head node. The receiving cluster head node ranks the priority of the access cluster head node. When there is abnormal data in the data packet of the cluster head, the receiving cluster head gives the highest priority to the abnormal data packet that is accessed and received. Congestion relief process based on path transfer (1) First, perform congestion detection on the connected cluster head node to determine the congestion situation; (2) Then set the flag bit to inform the source cluster head node of the congestion situation; (3) Finally, the cluster head node in the congested state is processed to relieve the congestion in the path.

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