A mine shaft group drilling and blasting construction state monitoring method based on multi-source sensor fusion

Abstract

The application discloses a mine shaft group drilling and blasting construction state monitoring method based on multi-source sensor fusion, and the method is as follows: sensors are arranged on a mine shaft group drilling and blasting construction site; a data acquisition and transmission module collects data of each sensor in real time and transmits the data to a data processing center; the data processing center removes outliers from original data and extracts feature information related to the drilling and blasting construction state; the feature information is subjected to data fusion processing, a comprehensive monitoring index is formed by using a weighted average method; the monitoring index is used to evaluate the construction state and to monitor the shaft drilling and blasting construction state in real time; the real-time monitoring result is fed back to a construction management system, used for adjusting and optimizing a construction plan, and the monitoring result is transmitted to construction personnel in the form of a chart so that the construction personnel can know the construction state at any time. The application improves the accuracy and reliability of the construction state evaluation.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of construction monitoring, and relates to a mine shaft group drilling and blasting construction state monitoring method, in particular to a mine shaft group drilling and blasting construction state monitoring method based on multi-source sensor fusion. BACKGROUND

[0002] Mining provides raw materials for modern industry and provides important basic support for scientific and technological development. At present, there are many mining enterprises in the world, but with the increasing depletion of shallow resources, mining gradually shifts to deep mining. Therefore, many open-pit mines also gradually develop towards underground mining. In the process of downward extension of the shaft, drilling and blasting method is generally used for construction. During the drilling and blasting construction process, the accuracy and real-time of construction state monitoring are of great significance to the safety and efficiency of construction. If there is a certain error in the evaluation of the construction state of the section, it not only increases the difficulty of the mine shaft group excavation project, but also brings great threat to the safety of the construction personnel and equipment.

[0003] At present, the general monitoring method is realized by a single sensor, and the data content monitored is limited, which cannot accurately evaluate the construction state from multiple aspects, and is prone to misjudgment. In the drilling and blasting construction monitoring, the use of a single sensor for construction state monitoring cannot meet the requirements.

[0004] Therefore, it is necessary to use multi-source sensors to monitor the drilling and blasting construction state, extract more characteristic information of the section construction, and perform multi-field information fusion processing and decision analysis for the mine shaft group drilling and blasting construction state monitoring and early warning. SUMMARY

[0005] In order to overcome the shortcomings of the background art, the present application provides a mine shaft group drilling and blasting construction state monitoring method based on multi-source sensor fusion.

[0006] The purpose of the present application is realized by the following technical scheme:

[0007] A mine shaft group drilling and blasting construction state monitoring method based on multi-source sensor fusion comprises the following steps:

[0008] Step 1: In the mine shaft group drilling and blasting construction site, according to the construction requirements, vibration sensors, stress sensors, temperature sensors, displacement sensors and photosensitive sensors are arranged at the corresponding positions, and the sensors are connected to the data acquisition and transmission module in a wireless manner;

[0009] Step 2: The data acquisition and transmission module collects the data of each sensor in real time and transmits it to the data processing center;

[0010] Step 3: The data processing center performs outlier rejection on the received raw data and extracts feature information related to the drilling and blasting construction state, including stress change, displacement change, temperature change, vibration frequency and photosensitive change;

[0011] Step 4: The feature information extracted from each sensor is subjected to data fusion processing in the data processing center using a distributed Kalman filter fusion analysis algorithm (DKF), and a weighted average method is used to form a comprehensive monitoring index, and the construction state is evaluated according to the monitoring index;

[0012] Step 5: According to the analysis of the monitoring index, the construction state is evaluated, and the vertical shaft drilling and blasting construction state is monitored in real time;

[0013] Step 6: The real-time monitoring results are fed back to the construction management system for adjusting and optimizing the construction plan, and the monitoring results are transmitted to the construction personnel to keep them informed of the construction state.

[0014] Compared with the prior art, the present application has the following advantages:

[0015] The present application uses multi-source sensor fusion to monitor the mine shaft drilling and blasting construction state from multiple angles and aspects, and the collected data is preprocessed before fusion analysis and comparison, effectively reducing the misjudgment influence of single sensor uncertainty factors, improving the accuracy and reliability of construction state evaluation; At the same time, the present application uses wireless data transmission, fast data transmission, fast analysis and comparison, timely warning information release and fast feedback, which can provide basis for optimizing construction scheme, reduce construction risk and avoid unnecessary loss. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is the flow chart of the mine shaft group drilling and blasting construction state monitoring method of the present application based on multi-source sensor fusion;

[0017] Figure 2 is a zigbee wireless transmission schematic diagram;

[0018] Figure 3 is a data fusion processing schematic diagram. DETAILED DESCRIPTION

[0019] The technical solutions of the present application will be further described below in conjunction with the drawings, but are not limited thereto, and any modification or equivalent replacement of the technical solutions of the present application without departing from the spirit and scope of the present application shall be covered in the protection scope of the present application.

[0020] The application provides a mine shaft group drilling and blasting construction state monitoring method based on multi-source sensor fusion, as shown in the figure. Figure 1 The method comprises the following steps:

[0021] Step 1: In the mine shaft group drilling and blasting construction site, according to the construction requirements, vibration sensors, stress sensors, temperature sensors, displacement sensors and photosensitive sensors are arranged at the corresponding positions, and the sensors are connected to the data acquisition and transmission module in a wireless manner.

[0022] In this step, the data collected by the stress sensor is used to analyze the stress change of the rock; the data collected by the displacement sensor is used to analyze the displacement of the rock; the temperature sensor is used to collect the temperature change of the drilling hole, reflecting the rock blasting condition; the vibration sensor is used to collect the vibration of the surrounding rock, reflecting the influence of the construction on the surrounding rock; and the photosensitive sensor is used to collect the dust condition in the well after blasting, to judge the ventilation condition and whether the next step of construction is to be carried out.

[0023] As shown in the figure, Figure 2 The data acquisition and transmission module adopts zigbee wireless communication technology, and the zigbee wireless communication module comprises a wireless gateway node and a plurality of terminal nodes. The number of the plurality of terminal nodes is the same as the number of the sensors, and the plurality of terminal nodes are connected with the stress sensors, the displacement sensors, the temperature sensors, the vibration sensors and the photosensitive sensors respectively.

[0024] Step 2: The data acquisition and transmission module collects the data of each sensor in real time and transmits it to the data processing center. In this process, the accuracy and integrity of the data need to be ensured at all times.

[0025] In this step, the data is transmitted to the database in a wireless receiving mode, and the corresponding receiving equipment and software need to be equipped to receive, store and process the related data.

[0026] Step 3: The data processing center removes the abnormal values of the received original data and extracts the characteristic information related to the drilling and blasting construction state, including stress change, displacement change, temperature change, vibration frequency and photosensitive change.

[0027] Step 4: The characteristic information extracted from each sensor is subjected to data fusion processing in the data processing center by using a distributed Kalman filter fusion analysis algorithm (DKF), and a comprehensive monitoring index is formed by using a weighted average method. The construction state is evaluated according to the monitoring index; the monitoring index comprehensively considers the characteristic information extracted by the above-mentioned sensors, and provides accuracy and reliability for the evaluation of the construction state.

[0028] In this step, the data fusion processing adopts multi-source sensor fusion technology, including data classification, feature extraction, data fusion and decision making, as shown in the following specific steps: Figure 3

[0029] Step 4-1: Local filtering processing

[0030] (1) Set five local filters, and the five local filters process the five sensors arranged in step 1 respectively, receive data from the five sensors, and z k1 is the stress change data, z k2 is the displacement change data, z k3 is the vibration change data, z k4 is the temperature change data, and z k5 is the photosensitive change data.

[0031] (2) After the local filter receives the data, the state at the current time is predicted according to the Kalman filter prediction step and the estimated covariance P k , the expression is as follows:

[0032]

[0033]

[0034] In the formula: represents the prior state estimation at time k, that is, the state at time k is predicted according to the optimal estimation at time k-1; A k is the state transition matrix; is the posterior state estimation at time k-1; B k is the control matrix; μ k is the control amount given to the system at time k; P k is the prior state estimation covariance; Q is the covariance matrix of process noise;

[0035] (3) According to the update step of Kalman filter, the observation data z k is used to update the prior state estimation and the prior state estimation covariance P k , the expression is as follows:

[0036]

[0037] In the formula: K k is the Kalman gain, H k is the observation matrix, R is the covariance matrix of observation noise, is the posterior state estimation at time k, P' k is the posterior state estimation covariance, z k is the observation value at time k;​

[0038] Step 4-2: Information fusion processing

[0039] The posterior state estimation of all local filters is processed by weighted average and the posterior state estimation covariance P' k The collected data is sent to the central node for result integration to obtain the global state estimation and the global estimation covariance P global,k , which is expressed as follows:

[0040]

[0041] In the formula: ω i is the weight of each local filter; N is the number of local filters.

[0042] Step 5: Evaluate the construction state according to the analyzed monitoring indicators, and monitor the shaft drill-and-blast method construction state in real time, with the specific steps as follows:

[0043] Step 5-1: Analyze, judge and evaluate the drill-and-blast construction state according to the monitoring indicators, and the evaluation content includes the stability of surrounding rock, the effect of section blasting, the affected situation of surrounding rock and the dust pollution in the shaft;

[0044] Step 5-2: The data processed by the distributed Kalman filter fusion analysis algorithm is fed back to the local filter as the initial value for adjusting the parameters of the local filter or as the prediction initial value of the next time step. Thus, with the passage of time, the algorithm will continuously iterate the local filtering and information fusion steps, and continuously update the drill-and-blast construction state, so as to monitor the shaft drill-and-blast construction state in real time and update the global state estimation in real time.

[0045] Step 6: The real-time monitoring results are fed back to the construction management system for adjusting and optimizing the construction plan, and the monitoring results are transmitted to the construction personnel to enable them to understand the construction state at any time.

[0046] In this step, the monitoring results are sent to the construction responsible person, technical personnel, personnel entering the shaft in the form of charts or reports, and uploaded to the cloud for saving.

Claims

1. A method for monitoring the drilling and blasting construction status of a mine shaft group based on multi-source sensor fusion, characterized in that The method comprises the following steps: Step 1: At the mine shaft drilling and blasting construction site, monitoring is carried out according to construction requirements. Vibration sensors, stress sensors, temperature sensors, displacement sensors, and light sensors are deployed at predetermined locations. These sensors are wirelessly connected to the data acquisition and transmission module. The stress sensor is used to analyze stress changes in the rock; the displacement sensor is used to analyze rock displacement; the temperature sensor is used to collect temperature changes in the borehole to reflect the rock blasting situation; the vibration sensor is used to collect vibrations in the surrounding rocks to reflect the impact of the construction on the surrounding rocks; and the light sensor is used to collect dust in the shaft after blasting to determine ventilation conditions and whether to proceed to the next construction step. Step 2: The data acquisition and transmission module collects data from each sensor in real time and transmits it to the data processing center; Step 3: The data processing center removes outliers from the received raw data and extracts characteristic information related to the drilling and blasting construction status, including stress changes, displacement changes, temperature changes, vibration frequency, and photosensitivity changes; Step 4: The feature information extracted from each sensor is fused and processed in the data processing center using a distributed Kalman filter fusion analysis algorithm. The weighted average method is used to form a comprehensive monitoring index based on the data. The construction status is evaluated based on the monitoring index. Step 5: Evaluate the construction status based on the analyzed monitoring indicators and monitor the shaft drilling and blasting construction status in real time; Step 6: Feedback the real-time monitoring results to the construction management system for adjusting and optimizing the construction plan. At the same time, the monitoring results are transmitted to the construction personnel so that they can always understand the construction status.

2. The method for monitoring the drilling and blasting construction status of a mine shaft group based on multi-source sensor fusion according to claim 1 is characterized in that The data acquisition and transmission module adopts a ZigBee wireless communication module, which includes a wireless gateway node and multiple terminal nodes. The number of the multiple terminal nodes is the same as the number of sensors. The multiple terminal nodes are respectively connected to the stress sensor, displacement sensor, temperature sensor, vibration sensor and photosensor.

3. The method for monitoring the drilling and blasting construction status of a mine shaft group based on multi-source sensor fusion according to claim 1 is characterized in that The specific steps of step 5 are as follows: Step 5-1: Analyze, judge and evaluate the drilling and blasting construction status based on monitoring indicators; Step 5-2: The data fused and processed by the distributed Kalman filter fusion analysis algorithm is fed back to the local filter as the initial value, which is used to adjust the parameters of the local filter or serve as the initial value for the prediction of the next time step. As time goes by, the algorithm will continuously iterate the local filtering and information fusion steps, continuously update the drilling and blasting construction status, thereby monitoring the vertical shaft drilling and blasting construction status in real time and updating the global state estimation in real time.

4. The method for monitoring the drilling and blasting construction status of a mine shaft group based on multi-source sensor fusion according to claim 3 is characterized in that In step 5-1, the evaluation contents include the stability of the surrounding rock, the effect of the cross-section blasting, the impact on the surrounding rocks, and the dust pollution in the well.

5. The method for monitoring the drilling and blasting construction status of a mine shaft group based on multi-source sensor fusion according to claim 1 is characterized in that In step 6, the monitoring results are sent to the construction manager, technicians, and construction personnel entering the well in the form of charts or reports and uploaded to the cloud for storage.

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