Real-time safety risk assessment and intelligent scheduling method for blasting engineering site

Abstract

The application provides a blasting engineering site safety risk real-time evaluation and intelligent scheduling method. In the application, a piezoelectric sensor array is arranged at a blasting engineering site to collect sound wave signals and capture dynamic change information of internal fissures of a rock mass; time difference data of sound wave propagation between adjacent nodes is calculated, and the spatial position of new fissures and the expansion trend of existing fissures are determined according to the time difference fluctuation characteristics; the fissure position, expansion trend and current blasting operation stage label are input into a fuzzy logic decision system, and a fissure risk distribution map reflecting the instantaneous stability of the rock mass is generated through collaborative analysis; the site safety risk level is determined based on the map, and the subsequent instability possibility of the rock mass is quantified; scheduling instructions are generated according to the risk level, and the drilling position, charging sequence and detonation timing are dynamically adjusted to optimize the operation process and avoid instability risks. The application improves the dynamic perception accuracy and response speed of the rock mass instability risk at the blasting engineering site.

Description

Technical Field

[0001] The present application relates to the technical field of risk assessment and intelligent scheduling, and in particular to a method for real-time assessment and intelligent scheduling of safety risks at blasting engineering sites. Background Art

[0002] The rock cracks at the blasting project site evolve dynamically with the construction progress. It is necessary to sense the spatial expansion trend of the cracks and integrate the characteristics of the blasting stage to predict risks, and synchronously adjust the drilling positioning, charging sequence and detonation timing to prevent and control rock instability accidents.

[0003] A network of fixed acoustic emission sensors is used to monitor rock fracture signals, and active fracture areas are identified based on waveform energy intensity thresholds. Alarms are triggered based on preset static risk levels, and operating parameters are adjusted based on manual experience.

[0004] Static judgment rules are difficult to adapt to the changes in rock disturbance state caused by the transition from drilling to blasting, resulting in frequent false alarms and missed alarms; the manual intervention mechanism causes a lag in risk response and cannot support rapid process adjustments before rock instability; the acoustic emission signal is disturbed by the vibration of construction machinery, and there is a significant deviation in crack positioning. Summary of the Invention

[0005] This application provides a real-time assessment and intelligent scheduling method for safety risks at blasting engineering sites, which is used to solve the problems of insufficient dynamic perception accuracy and response speed of rock instability risks at blasting engineering sites in the existing technology.

[0006] This application provides a real-time assessment and intelligent scheduling method for safety risks at blasting engineering sites, including:

[0007] A piezoelectric sensor array is deployed on the surface of the rock mass to be excavated at the blasting project site. The piezoelectric sensor array is used to continuously capture acoustic wave signals generated at different stages of the blasting operation. The acoustic wave signals contain information about the dynamic changes in the internal cracks of the rock mass.

[0008] Based on the acoustic wave signal, calculating the time difference data of the acoustic wave propagation between adjacent sensor nodes, and determining the spatial position of the newly formed cracks in the rock mass and the expansion trend of the existing cracks according to the change characteristics of the time difference data;

[0009] Inputting the spatial position, the expansion trend and the time sequence constraints of the current blasting operation into a fuzzy logic decision system for collaborative analysis to generate a fracture risk distribution map reflecting the immediate stability of the rock mass;

[0010] Determining the safety risk level faced by the blasting engineering site based on the immediate stability of the rock mass represented by the fracture risk distribution map, wherein the safety risk level quantitatively represents the possibility of the rock mass becoming unstable during subsequent blasting operations;

[0011] A scheduling instruction is generated based on the safety risk level, and the subsequent drilling operation position, charging operation sequence and detonation network timing of the blasting engineering site are dynamically adjusted through the scheduling instruction to optimize the on-site operation process and avoid potential rock instability risks.

[0012] Optionally, the spatial position, the expansion trend, and the timing constraints of the current blasting operation are input into a fuzzy logic decision system for collaborative analysis to generate a fracture risk distribution map reflecting the immediate stability of the rock mass, including:

[0013] Marking the timing constraints of the current blasting operation, where the timing constraints correspond to a drilling operation window, a charging operation window, or a detonation operation window;

[0014] Configuring an association rule base of the spatial position, the expansion trend, and the annotated temporal constraints in a fuzzy logic decision system, wherein the association rule base defines the allowable limit values ​​of the distance between the fissure position and the core area of ​​the blasting operation under different window periods;

[0015] A collaborative analysis operation of the spatial position, the expansion trend and the annotated temporal constraints is performed in the association rule base to generate a fracture risk distribution map annotating the instantaneous stability state of the rock mass.

[0016] Optionally, the step of calculating time difference data of sound wave propagation between adjacent sensor nodes based on the sound wave signal, and determining the spatial position of newly formed cracks and the expansion trend of existing cracks within the rock mass according to the change characteristics of the time difference data, includes:

[0017] Calculating the propagation time difference of the acoustic wave signal received by each pair of adjacent nodes in the piezoelectric sensor array to form a time difference data set of multiple groups of node pairs;

[0018] Extracting the fluctuation morphological features of the time difference data set during the blasting process, and identifying abnormal node pair combinations with sudden or continuous increase in time difference characteristics based on the fluctuation morphological features;

[0019] According to the physical position association relationship and the time difference growth rate of the abnormal node pair combination, the spatial position point set of the new crack and the expansion trend of the existing crack are mapped and generated.

[0020] Optionally, generating a scheduling instruction based on the safety risk level, and dynamically adjusting subsequent drilling operation positions, charging operation sequence, and detonation network timing at the blasting engineering site through the scheduling instruction to optimize the on-site operation process to avoid potential rock instability risks, including:

[0021] According to the safety risk level, a preset risk scheduling mapping rule is called to generate a scheduling instruction including a drilling position offset, a charging sequence adjustment list, and a detonation sequence delay value;

[0022] executing the scheduling instruction at the blasting engineering site, correcting the drilling operation position according to the drilling position offset, adjusting the charging operation sequence according to the reorganized charging sequence list, and updating the initiation network timing according to the initiation timing delay value;

[0023] Based on the corrected drilling operation position, the reorganized charging operation sequence, and the updated initiation network timing, an optimized operation process is constructed to avoid potential rock mass instability risks.

[0024] Optionally, based on the real-time stability of the rock mass represented by the crack risk distribution map, the safety risk level faced by the blasting engineering site is determined, which quantitatively represents the possibility of rock mass instability in subsequent blasting operations, including:

[0025] The crack risk distribution map is divided into rock mass spatial grid cells, and the instability probability level values recorded by the rock mass spatial grid cells are counted;

[0026] The dynamic influence factor of the rock mass spatial grid cell based on the blasting operation advancing direction is calculated, wherein the dynamic influence factor of the adjacent grid cell at the current construction position is higher than that of the distal grid cell;

[0027] The instability probability level values and the dynamic influence factors are superimposed to generate the safety risk level faced by the blasting engineering site, which quantitatively represents the possibility of rock mass instability in subsequent blasting operations.

[0028] Optionally, the fluctuation pattern feature of the time difference data set in the blasting process is extracted, and the abnormal node pair combination with time difference sudden increase or continuous growth feature is identified according to the fluctuation pattern feature, including:

[0029] The time difference data set of each pair of adjacent nodes in the piezoelectric sensor array during the blasting drilling stage, the charging stage, and the initiation stage is collected;

[0030] The fluctuation rising rate value and the fluctuation continuous growth time value of the time difference data set are extracted, wherein the fluctuation rising rate value represents the time difference growth amplitude per unit time, and the fluctuation continuous growth time value represents the continuous growth state duration time;

[0031] The node pair combination whose fluctuation rising rate value exceeds the sudden increase judgment threshold value and whose fluctuation continuous growth time value exceeds the continuous judgment threshold value is identified as an abnormal node pair combination.

[0032] Optionally, performing a collaborative analysis operation of the spatial position, the expansion trend, and the annotated temporal constraints in the association rule base to generate a fracture risk distribution map annotating the instantaneous stability state of the rock mass includes:

[0033] Mark the timing constraints of the current blasting operation and generate marked timing constraints;

[0034] Configuring an association rule base of the spatial position, the expansion trend, and the annotated temporal constraints, wherein the association rule base defines proximity limits between the crack position and the blasting core area under different window labels;

[0035] A collaborative analysis operation of the spatial position, the expansion trend and the annotated temporal constraints in the association rule base is performed to generate a fracture risk distribution map annotating the instantaneous stability state of the rock mass.

[0036] In an embodiment of the present application, a piezoelectric sensor array is deployed on the surface of a rock mass to be excavated at a blasting site. The piezoelectric sensor array is used to continuously capture acoustic signals generated at different stages of the blasting operation. The acoustic signals contain information on the dynamic changes in fractures within the rock mass. Based on the acoustic signals, time difference data of acoustic wave propagation between adjacent sensor nodes is calculated. The spatial location of newly created fractures within the rock mass and the expansion trend of existing fractures are determined based on the changing characteristics of the time difference data. The spatial location, the expansion trend, and the timing constraints of the current blasting operation are input into a fuzzy logic decision system for collaborative analysis to generate a fracture risk distribution map reflecting the immediate stability of the rock mass. Based on the immediate stability of the rock mass represented by the fracture risk distribution map, the safety risk level faced by the blasting site is determined. The safety risk level quantitatively represents the possibility of rock mass instability during subsequent blasting operations. A scheduling instruction is generated based on the safety risk level. The scheduling instruction dynamically adjusts the subsequent drilling operation location, charging operation sequence, and detonation network timing at the blasting site to optimize the on-site operation process and avoid potential rock mass instability risks.

[0037] The technical solution of this application has the following beneficial effects:

[0038] Capturing dynamic changes in rock fractures during each blasting phase addresses the inability of traditional point-based monitoring to cover the entire operation cycle. Converting acoustic signals into quantitative indicators of fracture spatial expansion overcomes the accuracy limitations of manual judgment of fracture trends. Integrating fracture status with blasting timing constraints generates risk distribution maps, eliminating the decision-making bias caused by static rules being unable to adapt to dynamic construction scenarios. Quantifying the potential for rock instability overcomes the lag in prevention and control measures caused by qualitative risk descriptions. Dynamically coordinating adjustments to drilling, charging, and detonation processes eliminates the response delay chain of traditional manual scheduling at the critical point of rock instability.

[0039] Furthermore, the drilling, charging, or detonation windows for blasting operations are labeled as timing constraints. A rule base is configured to associate the spatial location of fractures, their expansion trends, and window periods, defining the permissible distance limits between the fracture location and the blasting core area under different window periods. The three elements are then analyzed collaboratively within the rule base to generate a fracture risk distribution map that annotates the rock mass's immediate stability status. This allows for real-time assessment of fracture risk and dynamic matching of blasting stages, eliminating control failures caused by misjudgment of risks across different stages.

[0040] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0042] Figure 1 A flowchart of a method for real-time assessment and intelligent scheduling of safety risks at a blasting engineering site provided by the present application is shown;

[0043] Figure 2 The present invention provides a schematic diagram of a real-time safety risk assessment and intelligent scheduling system for blasting engineering sites;

[0044] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0046] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0047] Existing blasting rock monitoring solutions rely on acoustic emission sensor networking and static risk thresholds. Their core flaws lie in three contradictions: the waveform energy intensity threshold cannot adapt to the dynamic evolution of the rock disturbance pattern during the transition from drilling to blasting, resulting in frequent false alarms and missed alarms; the mechanism of manually adjusting operating parameters based on experience causes a lag in risk response, making it difficult to meet the rapid prevention and control needs of the critical point of rock instability; the frequency band overlap effect between the acoustic emission signal and the vibration of construction machinery causes crack positioning deviations, resulting in mistargeting of prevention and control measures.

[0048] In response to the above-mentioned defects, the present invention proposes a rock mass risk assessment method based on the collaboration of piezoelectric sensor arrays, acoustic time difference data, and fuzzy logic decision-making. Its core lies in: using the piezoelectric array to capture acoustic wave signals according to the blasting stage, analyzing the time difference data of adjacent nodes to invert the spatial expansion trend of the crack; combining the current operation window period label, dynamically matching the distance allowable limit between the crack position and the blasting core area in the fuzzy logic system, and generating a stage-adaptive crack risk distribution map; generating scheduling instructions based on the risk level, and jointly adjusting the drilling positioning, charging sequence, and detonation sequence. This method breaks through the dual limitations of static rules and manual decision-making - time difference data eliminates mechanical vibration interference and achieves improved crack positioning accuracy; the fuzzy rule library dynamically binds the operation stage to solve the risk misjudgment across cross-processes; scheduling instructions respond in seconds to block the critical chain reaction of rock mass instability, and fundamentally overcome the pain points of positioning deviation, response lag, and prevention and control failure in the existing technology.

[0049] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0050] Figure 1 The present invention provides a flowchart of a method for real-time assessment and intelligent scheduling of safety risks at a blasting engineering site, as shown in FIG. Figure 1 As shown, the method includes:

[0051] 101. Deploy a piezoelectric sensor array on the surface of the rock mass to be excavated at the blasting project site, and use the piezoelectric sensor array to continuously capture acoustic wave signals generated at different stages of the blasting operation, wherein the acoustic wave signals contain information on the dynamic changes of cracks within the rock mass;

[0052] In the above scheme, a piezoelectric sensor array refers to a detection device composed of multiple piezoelectric sensing units arranged in a grid pattern, which is used to convert mechanical vibrations into electrical signals. The different stages of a blasting operation include drilling, charging, and detonation. Acoustic signals are elastic stress waves released when cracks within the rock mass expand. Changes in their waveform reflect the dynamic evolution of the crack length and direction. Dynamic information about cracks within the rock mass includes three core parameters: the location of new cracks, the direction of existing cracks, and the rate of expansion.

[0053] In the embodiment of the present application, the monitoring area of ​​the rock surface is first determined: the blasting impact core area and the unstable structural zone are delineated on the surface of the rock to be excavated, and the sensor grid density is designed according to the area and shape of the area to ensure that the distance between adjacent sensors is less than the set ratio of the effective propagation distance of the sound wave, forming an array layout covering the target area.

[0054] Secondly, the array is deployed and bound to the blasting stage: the piezoelectric sensors are fixed to the rock surface according to the preset grid coordinates and connected to the central processing unit through cable networking. The blasting operation progress timing signal is synchronously connected. When entering the drilling stage, the low-frequency signal acquisition mode is started, the medium-frequency mode is enabled during the charging stage, and the high-frequency mode is activated during the detonation stage.

[0055] Finally, dynamic capture and information extraction: the array continuously receives rock vibration signals and generates a digital waveform sequence through analog-to-digital conversion; the waveform is segmented and stored based on the blasting stage label, and the arrival time, amplitude extreme value and frequency distribution characteristics of each waveform are extracted; the fracture activity events are identified based on the amplitude mutation points, and the environmental noise is separated by combining the frequency characteristics, and the dynamic change data set containing the fracture position mark and the expansion direction vector is output.

[0056] For example, during the drilling stage (low-frequency mode), the sensor captured a continuous vibration signal with an amplitude of 0.5V and a frequency of 80Hz. After analysis, it was determined to be the background noise of the drilling rig operation and was filtered out; a pulse signal with an amplitude of 1.2V and a frequency of 210Hz was simultaneously detected. According to the propagation path, it was located as a stress wave released by a newly formed crack 1.2 meters away from the S03 node of the sensor group. This event was marked as a dynamically changing data entry.

[0057] 102. Based on the acoustic wave signal, calculate the time difference data of acoustic wave propagation between adjacent sensor nodes, and determine the spatial location of new cracks in the rock mass and the expansion trend of existing cracks according to the change characteristics of the time difference data;

[0058] Optionally, step 102 may specifically include the following steps:

[0059] 1021. Calculate the propagation time difference of each pair of adjacent nodes in the piezoelectric sensor array when receiving the acoustic wave signal, and form a time difference data set of multiple groups of node pairs;

[0060] 1022、extracting fluctuation pattern features of the time difference data set in the blasting process, and identifying abnormal node pair combinations with time difference sudden increase or continuous growth features according to the fluctuation pattern features;

[0061] The step 1022 can specifically include the following processes: collecting time difference data sets of each pair of adjacent nodes in the piezoelectric sensor array in the blasting drilling stage, the charging stage and the detonation stage; extracting fluctuation rising rate values and fluctuation continuous growth time length values of the time difference data sets, wherein the fluctuation rising rate values represent the time difference growth amplitude per unit time, and the fluctuation continuous growth time length values represent the continuous growth duration; identifying node pair combinations with the fluctuation rising rate values exceeding a sudden increase judgment threshold and the fluctuation continuous growth time length values exceeding a continuous judgment threshold, and marking the node pair combinations as abnormal node pair combinations.

[0062] 1023、mapping and generating a spatial position point set of the new-born crack and an extension trend of the existing crack according to the physical position correlation and the time difference growth amplitude of the abnormal node pair combinations.

[0063] In the above scheme, the time difference data refer to the time difference values of the sound waves propagating from the same crack event to two adjacent sensors. The fluctuation pattern features include the time difference sudden increase feature and the continuous growth feature. The abnormal node pair combination refers to the sensor node group that monitors the features. The spatial position point set is a spatial coordinate set of the new-born crack. The extension trend describes the extension direction and speed of the existing crack.

[0064] In the embodiments of the present application, first, in step 1021, the time difference values of the same crack sound wave signals received by each pair of nodes in the piezoelectric sensor array that meet the adjacent condition, i.e., the node combinations with a distance less than the effective detection distance of the sound wave, are calculated: the accurate time when the node A and the node B record the arrival of the sound wave signal is obtained, the two time points are subtracted to obtain the time difference, and all adjacent node pairs are traversed to generate a time difference data set indexed by node pair number. For example, the node P01 records the arrival time of the sound wave as 1532.6 milliseconds, and the adjacent node P02 records the time as 1533.3 milliseconds, then the P01-P02 node pair time difference is 0.7 milliseconds.

[0065] Then, in step 1022, the time difference dataset is analyzed by blasting stage segmentation: the fluctuation characteristics of the time difference sequence of each node pair are extracted during the drilling stage, charging stage, and detonation stage. For the sudden increase characteristic, the time difference increase between adjacent acquisition cycles is tested to see if it exceeds the preset sudden increase threshold. For the sustained increase characteristic, the time difference increase is determined to see if it remains monotonically increasing for more than three consecutive cycles and the total increase exceeds the sustained threshold. Node pairs that meet any of these characteristics are marked as anomalous. For example, the time difference sequence of node pair S07-S08 during the charging stage is [0.4ms, 0.4ms, 1.5ms]. If the increase between adjacent cycles reaches 1.1ms, which is greater than the 0.5ms sudden increase threshold, it is marked as anomalous.

[0066] Finally, in step 1023, a spatial topological network is constructed based on the physical coordinates of the abnormal node pairs. If a single node pair exhibits a sudden increase, the coordinates of a new fracture are generated within a circle with the midpoint of the connecting line as the center and the time difference increase as the radius. If multiple adjacent node pairs simultaneously exhibit sustained growth, the direction of the existing fracture extension is determined based on the direction of the increase gradient, and an extension trajectory vector is generated starting from the position of the node pair with the largest increase. For example, if the abnormal node pair P12 (10m, 20m)-P13 (12m, 20m) increases by 1.2ms, the coordinates of a new fracture are generated with the midpoint (11m, 20m) as the center and a radius of 1.2 meters (11.3m, 20.2m). If the adjacent node pair P13-P14 increases from 0.8ms to 1.6ms and the coordinates extend westward, it is determined that the fracture is expanding westward at a rate of 0.4m / cycle.

[0067] In practical applications, during blasting in iron ore tunnels, a 6×4 piezoelectric sensor array is deployed in step 1021. Time differences between adjacent nodes are calculated. For example, the time difference between nodes A1 and A2 is 0.3 milliseconds during the drilling phase, increasing to 0.9 milliseconds during the charging phase. The time difference between nodes B2 and B3 increases continuously from 0.5 milliseconds to 1.1 milliseconds. This generates a dataset of 24 node-pair time differences.

[0068] By implementing step 1022 and analyzing the charging phase data, the time difference of the A1-A2 node pair increases from 0.3ms to 0.9ms, with an increase of 0.6ms, which is greater than the sudden increase threshold of 0.4ms, and is marked as abnormal; the time difference sequence of the B2-B3 node pair [0.5ms, 0.7ms, 0.9ms, 1.1ms] increases for 4 consecutive cycles and the total increase of 0.6ms is greater than the continuous threshold of 0.5ms, and is synchronously marked as abnormal.

[0069] Through the implementation of step 1023, the sudden increase feature of the A1 (5m, 0m)-A2 (7m, 0m) node pair, with the midpoint (6m, 0m) as the center and an increase of 0.6 meters as the radius, generates a new crack point (6.2m, 0.3m); the amplification gradient of the B2 (8m, 2m)-B3 (10m, 2m) and adjacent B3-B4 (12m, 2m) node pairs is 0.4ms→0.6ms, increasing from east to west, and it is determined that the existing crack is extending westward from the coordinate (9m, 2m) at a speed of 0.3m / cycle.

[0070] The overall solution of step 102 above achieves accurate positioning of the spatial position of new cracks and quantitative determination of the directional rate of expansion of existing cracks by dynamically analyzing the time difference fluctuation characteristics of sound wave propagation at each stage of blasting. This breaks through the limitations of traditional methods in misjudging the dynamic evolution of cracks and provides a data basis for the prevention and control of rock instability risks.

[0071] 103. Input the spatial position, the expansion trend, and the timing constraints of the current blasting operation into a fuzzy logic decision system for collaborative analysis to generate a fracture risk distribution map reflecting the immediate stability of the rock mass;

[0072] Optionally, step 103 may specifically include the following steps:

[0073] 1031. Mark the timing constraints of the current blasting operation, where the timing constraints correspond to a drilling operation window, a charging operation window, or a detonation operation window.

[0074] 1032. Configuring an association rule base for the spatial position, the expansion trend, and the annotated temporal constraints in a fuzzy logic decision system, wherein the association rule base defines the allowable distance limits between the fissure position and the core area of ​​the blasting operation under different window periods;

[0075] 1033. Perform a collaborative analysis operation of the spatial position, the expansion trend, and the annotated temporal constraints in the association rule base to generate a fracture risk distribution map annotating the instantaneous stability status of the rock mass.

[0076] Among them, step 1033 may specifically include the following processes: marking the timing constraints of the current blasting operation and generating the marked timing constraints; configuring an association rule library of the spatial position, the expansion trend and the marked timing constraints, the association rule library defining the proximity limit between the crack position and the blasting core area under different window labels; performing a collaborative analysis operation of the spatial position, the expansion trend and the marked timing constraints in the association rule library to generate a crack risk distribution map that marks the instantaneous stability status of the rock mass.

[0077] In the above scheme, the timing constraint refers to the stage labels used to divide the blasting construction process. The distance tolerance is a safety distance standard that is dynamically adjusted based on the blasting stage. The association rule base is a set of decision rules that stores the correspondence between stage characteristics and fracture risk. The collaborative analysis operation refers to the fusion decision-making process that simultaneously processes spatial location, expansion direction, and stage labels. The fracture risk distribution map is a visual atlas that labels the stability level based on the rock mass spatial grid.

[0078] In the embodiment of the present application, first, step 1031 is performed to analyze the status signal of the blasting site equipment: when the drilling rig starting current signal reaches the threshold, the drilling window period is marked; when the charging vehicle conveyor belt speed sensor is triggered, the charging window period is marked; when the detonator charging indicator light is on, the detonation window period is marked, and a stage label with a precise timestamp is generated.

[0079] Then, in step 1032, a three-dimensional decision rule base is constructed within the fuzzy logic system. For each window type, a safety threshold for the distance between the fracture location and the current core operating area is set. During the drilling window, a circular safety zone (e.g., a 5-meter radius) is established centered on the drilling point. During the charging window, a conical risk zone (e.g., a 3-meter radius plus an expansion direction correction) is established centered on the charging hole. During the detonation window, a strict exclusion zone (e.g., a 1-meter radius) is established centered on the detonation network node. An expansion trend influence coefficient is also configured, doubling the risk weight when the expansion direction points toward the core area.

[0080] Finally, through step 1033, the input fracture spatial position coordinates, expansion trend vector, and current window period label are sent to the rule library: the spatial Euclidean distance between each fracture point and the nearest core area is calculated; the risk correction factor is calculated based on the expansion trend direction. For example, if the angle between the trend angle and the core area orientation is less than 15°, the risk value is multiplied by 1.5; the trend correction is superimposed based on the threshold interval (safe / warning / dangerous) that the distance value falls into, and the corresponding position of the rock mass three-dimensional grid model is marked with red / yellow / blue risk levels.

[0081] In practical applications, during blasting in a lead-zinc mine, at step 1031, the drill rig hydraulic pressure is monitored to rise to 20 MPa at 9:15, marking the start of the drilling window. At 11:40, the charging tube pressure sensor reaches 0.8 MPa, switching the charging window. At 1:20 PM, the initiator voltage meter displays 200V, marking the detonation window. Labels for each stage are transmitted to the decision-making system.

[0082] Configure the phased rule base through step 1032: Drilling window period: fracture ≤ 3 meters from the drilling point → high risk; 3-5 meters → medium risk; > 5 meters → low risk; Charging window period: fracture ≤ 2 meters from the charging hole and trending toward the charging hole → high risk; ≤ 2 meters but trend diverging → medium risk; 2-3 meters → low risk; Detonation window period: fracture ≤ 1 meter from the detonation point → highest risk (red); 1-1.5 meters → high risk (orange).

[0083] Through step 1033, the crack point F (85m, 120m) detected during the charging window period is: 2.1 meters away from the No. 3 charging hole, falling into the 2-3 meter range → basic low risk, the expansion trend azimuth is 285°, and the charging hole is located at 280° → the angle is 5°<15° → risk correction × 1.5 → upgraded to medium risk, marked in yellow at the grid coordinates (85, 120); the crack point G (88m, 118m) during the detonation window period is: 0.9 meters ≤ 1 meter away from the detonation node → the highest risk, the expansion trend is facing the detonation network, directly triggering a red warning, and marked in dark red at the grid coordinates (88, 118).

[0084] It should be noted that the fracture risk distribution map is a visual atlas based on the output of the fuzzy logic decision-making system. It uses a three-dimensional spatial grid of the rock mass as a reference, with risk levels marked at the center of each grid cell (e.g., red / yellow / blue color mapping corresponds to high / medium / low risk). This map directly reflects the immediate stability status of the rock mass. The risk levels marked on the distribution map can be understood as initial risk levels, while the safety risk levels determined in the subsequent step 104 can be understood as optimized risk levels.

[0085] In step 103, a distribution map is generated by collaboratively analyzing spatial location, expansion trends, and temporal constraints (as described in step 1033). The risk level of each grid location (e.g., a red dot indicates a high-risk area) directly identifies the immediate stability of the rock mass at that location. A high-risk area indicates that the rock mass at that point is unstable (high probability of instability), while a low-risk area indicates stability (low probability of instability). In this embodiment, during the detonation window, fracture point G is 0.9 meters away from the detonation node and is marked as the highest risk (red), directly indicating that the immediate stability of the rock mass at that location is poor. Similarly, during the charging window, fracture point F is marked as medium risk (yellow), indicating that the stability of this location is medium.

[0086] The risk level in the distribution map integrates the dynamic influence of fracture location and expansion trend (e.g., risk is multiplied when the trend points to the core area), and is dynamically adjusted based on the distance tolerance limit during the blasting window (drilling, charging, or detonation) (e.g., the rule base in step 1032). Therefore, the distribution map is a spatial representation of the instantaneous stability of the rock mass. Its essence is to map the possibility of rock mass instability to risk labels for specific grid cells.

[0087] The overall solution of step 103 above, through the deep coupling of the dynamic rules of the blasting stage and the spatial evolution characteristics of the fracture, realizes the accurate scenario-based assessment of the rock stability risk, breaks through the adaptive limitations of the static threshold to the process conversion, and provides a decision-making basis for the accurate prevention and control of high-risk areas.

[0088] 104. Determine the safety risk level faced by the blasting engineering site based on the instantaneous stability of the rock mass represented by the fracture risk distribution map, wherein the safety risk level quantitatively represents the possibility of the rock mass becoming unstable during subsequent blasting operations;

[0089] Optionally, step 104 may specifically include the following steps:

[0090] 1041. Divide the fracture risk distribution map into rock mass spatial grid units, and count the instability probability level values ​​recorded in the rock mass spatial grid units;

[0091] 1042. Calculate the dynamic impact factor of the rock mass spatial grid unit based on the blasting operation advancement direction, wherein the dynamic impact factor of the adjacent grid unit at the current construction position is higher than that of the distal grid unit;

[0092] 1043. Superimpose the instability probability level value and the dynamic impact factor to generate a safety risk level faced by the blasting engineering site. The safety risk level quantitatively represents the possibility of rock mass instability in subsequent blasting operations.

[0093] In the above scheme, the rock mass spatial grid cell refers to a fixed-size grid of the rock mass to be excavated. The instability probability level is the quantitative stability value assigned to each grid cell in the risk distribution map. The blasting operation advance direction refers to the spatial movement path of the drilling, charging, and detonation processes. The dynamic impact factor represents the risk weight coefficient assigned to each grid cell based on its distance from the current construction location.

[0094] In this embodiment, the fracture risk distribution map is first loaded into the 3D modeling platform in step 1041. The rock mass is automatically sliced ​​into grid cells according to preset dimensions. The instability probability annotated at the center of each cell is scanned to construct a database mapping grid numbers to probability values. For example, a rock mass 20 meters long, 15 meters wide, and 10 meters high is segmented into 3000 grid cells, with the center of cell G201 annotated with a probability value of 0.75.

[0095] Then, in step 1042, the construction coordinates of the blasting operation are obtained, and a three-dimensional spatial coordinate system is established with this point as the center. Based on the straight-line distance between the grid cell center point and the construction coordinates, the dynamic impact factor is calculated using an inverse proportional function: for distances 0-2 meters, the factor is 1.0, for distances 2-5 meters, the factor is 0.6, for distances 5-10 meters, the factor is 0.3, and for distances greater than 10 meters, the factor is 0.1. The distance values ​​are calculated using the spatial rectangular coordinate system distance formula. For example, if the current charge point coordinates are (50m, 60m, 0m), the distance to the center of grid G305 (51m, 61m, 0m) is 1.41 meters, which results in a factor of 1.0; the distance to grid G410 (55m, 65m, 0m) is 7.07 meters, which results in a factor of 0.3.

[0096] Finally, step 1043 traverses each grid cell and performs weighted fusion: The cell's instability probability is read and multiplied by the corresponding dynamic impact factor to generate a weighted risk value. The weighted risk values ​​of all grid cells are accumulated and divided by the total number of grid cells to output a comprehensive safety risk level between 0 and 1. For example, the probability of grid G201 is 0.75 × the factor 0.6 = 0.45; the probability of grid G305 is 0.3 × 1.0 = 0.3; the comprehensive level = (0.45 + 0.3 + ...) / 3000 = 0.52, indicating medium-high risk.

[0097] In a practical application, during a copper mine tunnel excavation project, the rock mass area to be blasted, measuring 30 m long, 6 m wide, and 4 m high, was divided into 720 1 m³ cubes in step 1041. Scanning fracture risk distribution data revealed that the vault grid V101 was labeled with a probability value of 0.9 (high risk), the left sill grid L205 with a probability value of 0.4 (medium risk), and the floor grid B302 with a probability value of 0.1 (low risk). A table was created to map the correspondence between grid numbers and probability values.

[0098] At step 1042, the current drilling equipment coordinates (15m, 3m, 1.5m) are located. The spatial distance between each grid center point and the drilling point is calculated: the adjacent grid D101 (15m, 3m, 1.5m) has a distance of 0 meters, which results in a dynamic impact factor of 1.0; the medium-distance grid L205 (18m, 1m, 2m) has a distance of 3.6 meters, which results in a factor of 0.6; and the long-distance grid V101 (10m, 3m, 3.5m) has a distance of 5.2 meters, which results in a factor of 0.3.

[0099] A weighted calculation is performed through step 1043: V101: 0.9×0.3=0.27; L205: 0.4×0.6=0.24; B302: 0.1×0.3=0.03; D101: 0.6×1.0=0.6; comprehensive risk level = (0.27+0.24+0.03+0.6+...) / 720=0.38, generating a safety risk level of 0.38 / medium risk, triggering a yellow warning instruction.

[0100] It should be noted that in step 1041, the fracture risk distribution map is divided into fixed-size rock space grid units (such as 1m³ cubes in the embodiment), and each grid unit records a "instability probability level value", which is a quantitative value extracted from the distribution map (for example, a value in the range of 0-1, 0 represents complete stability and 1 represents complete instability). This value is the numerical form of the initial risk level in the distribution map, such as high risk corresponds to 0.8-1.0, medium risk corresponds to 0.4-0.7, and low risk corresponds to 0.1-0.3.

[0101] The instability probability level value is the result of the transformation of the fracture risk distribution map. The risk level (color marking) in the distribution map is extracted in step 1041 and counted as the value of each grid unit (such as in the lead-zinc mine case, the grid unit V101 marked with a probability value of 0.9 indicates a high-risk area for the vault). This value directly corresponds to the instantaneous stability value of the rock mass in the grid unit. The higher the value, the lower the instantaneous stability of the rock mass at that location (the higher the possibility of instability). The distribution map uses spatial grids to mark the stability status (such as the red risk area), and the instability probability level value is the numericalization (quantification) of the marking, which is convenient for subsequent calculations. It is clearly pointed out in the embodiment that after the distribution map is scanned, "a mapping database of grid numbers and probability values ​​is constructed." For example, in the copper mine case, the instability probability value of grid V101 is 0.9, indicating that its immediate stability is poor.

[0102] The safety risk level quantifies the likelihood of rock mass failure during subsequent blasting operations (as defined in the claims). It is based on the superposition of the distribution map (converted to numerical values ​​of failure probability) and the blasting progress dynamics. It can be understood as the initial risk level after treatment, or the final risk level.

[0103] The overall solution of step 104 above, by integrating the static risk distribution of rock mass and the dynamic position weight of construction, realizes the accurate spatiotemporal quantitative assessment of safety risk level, breaks through the traditional method's neglect of changes in construction process, and provides a decision-making basis for targeted prevention and control in high-risk areas.

[0104] 105. Generate a scheduling instruction based on the safety risk level, and dynamically adjust the subsequent drilling operation position, charging operation sequence and detonation network timing at the blasting engineering site through the scheduling instruction to optimize the on-site operation process and avoid potential rock instability risks.

[0105] Optionally, step 105 may specifically include the following steps:

[0106] 1051. Invoke a preset risk scheduling mapping rule according to the safety risk level to generate a scheduling instruction including a drilling position offset, a charging sequence adjustment list, and a detonation sequence delay value;

[0107] 1052. Execute the scheduling instruction at the blasting engineering site, correct the drilling operation position according to the drilling position offset, re-arrange the charging operation sequence according to the charging sequence adjustment list, and update the detonation network sequence according to the detonation sequence delay value;

[0108] 1053. Based on the revised drilling operation position, the reorganized charging operation sequence and the updated detonation network timing, an optimized operation process is constructed to avoid potential rock instability risks.

[0109] In the above scheme, the risk scheduling mapping rule is a predefined correspondence table between risk levels and operating parameters; the drilling position offset is the avoidance distance that the drilling point needs to move horizontally; the charging sequence adjustment list is an instruction set for rearranging the execution order of the charging holes; the detonation sequence delay value is the delayed detonation time added for a specific detonation point.

[0110] In this embodiment, step 1051 first matches the inputted security risk level against a pre-set rule base. The rule base associates different drilling offset distances, charge sequence reorganization logic, and detonation delay parameters for three level intervals: low (0-0.3), medium (0.3-0.7), and high (0.7-1). The system automatically searches for matching intervals, extracts the corresponding offset, charge sequence code, and delay value, and combines them to generate a structured dispatch instruction. For example, a risk level of 0.65 matches the medium risk rule, which calls for a drilling offset of 3 meters, a charge sequence of [4, 2, 1, 3], and a detonation delay of 200 milliseconds.

[0111] Then, in step 1052, the scheduling instructions are broken down and transmitted to the on-site equipment: the drilling rig positioning system receives the coordinate offset, superimposes the offset vector on the original drilling point coordinates, and automatically plans the new drilling path; the charging vehicle control unit parses the charging sequence code and reorders the charging hole access queue; the detonator programmer loads the delay parameters and inserts the specified delay into the detonator network timing table. For example, the original drilling point (100m, 50m) → the new point (103m, 50m); the charging sequence is changed from [1, 2, 3, 4] to prioritize hole 4; and the detonation time for hole 3 is adjusted from T0 to T0 + 200 milliseconds.

[0112] Finally, in step 1053, all adjusted parameters are integrated to generate a standardized operation process. The new drilling coordinate set, charging process flow, and detonation sequence are reorganized according to construction logic, and an executable solution document is output, including spatial avoidance paths, process execution sequence, and time control nodes. For example, the document clearly states "Drilling point coordinate set: [(103m, 50m), (105m, 52m)...]; Charging sequence: Hole 4 → Hole 2 → Hole 1 → Hole 3; Detonation sequence: Hole 3, delay 200 milliseconds."

[0113] In actual applications, in an underground gold mine blasting scenario, through step 1051, a safety risk level of 0.72 triggers a high-risk rule: Calling the rule: Drill offset 5.0 meters to avoid high-risk fissure areas; adjust the charging sequence to [3,1,4,2], and finally process hole 2 in the high-risk area; add a 300-ms detonation delay to hole 1 to avoid stress superposition; Generate the command: "Drill offset (ΔX=+5.0m); charging sequence [3,1,4,2]; delay +300ms for hole 1."

[0114] Through step 1052, the drilling rig positioning system: the original drilling point (80m, 120m) receives the X-axis +5m offset instruction → automatically locates the drilling point (85m, 120m); the charging vehicle control system: parses the sequence code [3, 1, 4, 2] → prioritizes navigation to hole No. 3 (75m, 110m) for charging; the detonator programmer: inserts "Basic Delay + 300ms" in the detonation parameter column of hole No. 1 → the original T0 detonation is changed to T0 + 300ms.

[0115] Step 1053 constructs an optimized operation process: Spatial avoidance: Generates a new borehole coordinate table [Hole 1 (85m, 120m), Hole 2 (90m, 125m)...]; Process reorganization: Develops a charging route map: Hole 3 → Hole 1 → Hole 4 → Hole 2; Timing control: Develops a detonation sequence: Hole 3 T0, Hole 4 T0+100ms, Hole 1 T0+300ms, Hole 2 T0+400ms; Outputs the "Blasting Operation Optimization Plan V3.0" to guide on-site construction. Construction is carried out according to the new plan: Hole 1 is drilled at 85 meters; the charging vehicles operate in the sequence [3→1→4→2]; and the detonation network implements delayed detonation for Hole 1. The rock instability risk warning is lifted.

[0116] The overall solution of step 105 above, through the risk-level-driven parameterized instruction generation and equipment collaborative execution mechanism, achieves full-process optimization of precise avoidance of drilling positioning, scientific reorganization of charging procedures, and active regulation of detonation sequence, thus building a closed loop for active prevention and control of rock instability risks.

[0117] The following is a complete embodiment of steps 101 to 105:

[0118] In the open-pit iron ore bench blasting scenario, an array of 64 piezoelectric sensors was deployed in an 8×8 grid in the 30-meter-high bench blasting area of ​​the iron ore mine through step 101. During the drilling phase, the low-frequency mode (0-200Hz) was activated, capturing the drill rig vibration background noise with an amplitude of 1.2V and a frequency of 85Hz. During the charging phase, the medium-frequency mode (200-500Hz) was switched to detect an acoustic pulse signal with an amplitude of 3.8V and a frequency of 350Hz near charging hole No. 3. Signal separation identified this as a release wave from newly formed fractures within the rock mass. During the detonation preparation phase, the high-frequency mode (500-1000Hz) was activated, recording a transient wave with an amplitude of 5.1V and a frequency of 780Hz, which was identified as a characteristic signal of accelerated fracture expansion 2.3 meters from sensor node S07.

[0119] Step 102 calculates the time differences between adjacent nodes: During the charge phase, the time difference between nodes P05 and P06 increases suddenly from 0.4ms to 1.6ms, with an increase of 1.2ms exceeding the 0.5ms sudden increase threshold. The time difference sequence between nodes P12 and P13 (0.8ms, 1.0ms, 1.3ms, 1.9ms) continuously increases, with a total increase of 1.1ms exceeding the 0.8ms sustained threshold. Both pairs are marked as abnormal nodes. Based on the coordinates of P05 and P06 (35m, 42m) and the 1.2ms increase, the coordinates of the newly formed fracture are generated (35.3m, 42.2m) with the midpoint of the line connecting the nodes as the center. Based on the increase gradient between P12 and P13 and the adjacent P13 and P14 (0.8ms→1.1ms→1.9ms from west to east), it is determined that the existing fracture is expanding eastward at a rate of 0.5m / cycle.

[0120] After step 103, the current stage is labeled as the charging window period. The rule base defines: if the distance from the charging hole is ≤3 meters and the trend points toward the charging hole, the risk is high. Fracture point A (35.3m, 42.2m) is 2.9 meters away from charging hole 3, which is less than the 3-meter limit. The extended trend azimuth is 85°, and the charging hole is located at 80°, with an angle of 5° less than 15°, triggering a high-risk assessment. Fracture point B (38.0m, 40.5m) is 3.2 meters away from the charging hole, which is greater than 3 meters, but the trend points toward the charging hole, with an angle of 10°, resulting in a medium-risk assessment. Red / yellow risk indicators are marked at the corresponding coordinates on the 3D rock mass grid model.

[0121] Step 104 divides the rock mass into 1m³ grid cells. Cell G201 (35.3m, 42.2m) has a probability of instability of 0.85, and cell G305 (38.0m, 40.5m) has a probability of instability of 0.65. Centered on the current charge point (35m, 42m), G201 is 0.5m away, resulting in a dynamic impact factor of 1.0; G305 is 3.5m away, resulting in a factor of 0.6. Weighted calculation: 0.85 × 1.0 = 0.85, 0.65 × 0.6 = 0.39. The overall risk level is (0.85 + 0.39 + ...) / Total number of grid cells = 0.58, resulting in a medium-high risk.

[0122] In step 105, the medium-to-high risk level of 0.58 triggers the scheduling rule: the drilling point is shifted by 3 meters: the original drilling point (40m, 45m) → the new point (43m, 45m); the charging sequence is adjusted from the original sequence [1, 2, 3] to [3, 1, 2], prioritizing the low-risk hole 3; the detonation delay is set: a 200 millisecond delay is added to hole 3; the drill rig drills at (43m, 45m); the charging vehicle operates in the sequence [3 → 1 → 2]; and the detonation network adjusts the detonation time for hole 3 from T0 to T0 + 200ms. An optimized operation process document is generated, clearly noting the new drilling coordinate set, charging path diagram, and detonation sequence.

[0123] Figure 2 The present invention provides a schematic diagram of a real-time safety risk assessment and intelligent scheduling system for a blasting project site, as shown in FIG. Figure 2 As shown, the system includes:

[0124] Capture module 21, for deploying a piezoelectric sensor array on the surface of the rock mass to be excavated at the blasting engineering site, and using the piezoelectric sensor array to continuously capture acoustic wave signals generated at different stages of the blasting operation, the acoustic wave signals containing dynamic change information of cracks within the rock mass;

[0125] A calculation module 22 is configured to calculate time difference data of acoustic wave propagation between adjacent sensor nodes based on the acoustic wave signal, and determine the spatial location of new cracks within the rock mass and the expansion trend of existing cracks according to the change characteristics of the time difference data;

[0126] An input module 23 is configured to input the spatial position, the expansion trend, and the timing constraints of the current blasting operation into a fuzzy logic decision system for collaborative analysis to generate a fracture risk distribution map reflecting the immediate stability of the rock mass;

[0127] A determination module 24 is configured to determine a safety risk level faced by the blasting engineering site based on the instantaneous stability of the rock mass represented by the fracture risk distribution map, wherein the safety risk level quantitatively represents the possibility of the rock mass becoming unstable during subsequent blasting operations;

[0128] The adjustment module 25 is used to generate a scheduling instruction based on the safety risk level, and dynamically adjust the subsequent drilling operation position, charging operation sequence and detonation network timing of the blasting engineering site through the scheduling instruction to optimize the on-site operation process to avoid potential rock instability risks.

[0129] Figure 2 The blasting engineering site safety risk real-time assessment and intelligent scheduling system can perform Figure 1The implementation principles and technical effects of the real-time blasting site safety risk assessment and intelligent scheduling method described in the illustrated embodiment will not be elaborated upon. The specific manner in which the various modules and units of the real-time blasting site safety risk assessment and intelligent scheduling system in the aforementioned embodiment perform their operations has been described in detail in the relevant embodiments of the method and will not be elaborated upon here.

[0130] In one possible design, Figure 2 The embodiment of the blasting engineering site safety risk real-time assessment and intelligent scheduling system can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0131] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0132] The processing component 32 is used for the above Figure 1 The embodiment provides a method for real-time assessment and intelligent scheduling of safety risks at blasting engineering sites.

[0133] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0134] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0135] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0136] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0137] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0138] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0139] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment provides a method for real-time safety risk assessment and intelligent scheduling at a blasting engineering site.

[0140] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0141] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0142] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer or server) to execute the methods described in each embodiment or certain portions of the embodiments.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A real-time assessment and intelligent scheduling method for safety risks at blasting engineering sites, characterized by: include: A piezoelectric sensor array is deployed on the surface of the rock mass to be excavated at the blasting project site. The piezoelectric sensor array is used to continuously capture acoustic wave signals generated at different stages of the blasting operation. The acoustic wave signals contain information about the dynamic changes in the internal cracks of the rock mass. Based on the acoustic wave signal, calculating the time difference data of the acoustic wave propagation between adjacent sensor nodes, and determining the spatial position of the newly formed cracks in the rock mass and the expansion trend of the existing cracks according to the change characteristics of the time difference data; The spatial position, the expansion trend, and the timing constraints of the current blasting operation are input into a fuzzy logic decision system for collaborative analysis to generate a fracture risk distribution map reflecting the immediate stability of the rock mass. The fracture risk distribution map is labeled with initial risk levels by rock mass spatial grid cells, and the color corresponding to the initial risk level of each grid cell position represents the immediate stability of the rock mass at that location; Determining the safety risk level faced by the blasting engineering site based on the immediate stability of the rock mass represented by the fracture risk distribution map, wherein the safety risk level quantitatively represents the possibility of the rock mass becoming unstable during subsequent blasting operations; A scheduling instruction is generated based on the safety risk level, and the subsequent drilling operation position, charging operation sequence and detonation network timing of the blasting engineering site are dynamically adjusted through the scheduling instruction to optimize the on-site operation process and avoid potential rock instability risks.

2. The method according to claim 1, characterized in that The spatial position, the expansion trend, and the timing constraints of the current blasting operation are input into the fuzzy logic decision system for collaborative analysis to generate a fracture risk distribution map reflecting the immediate stability of the rock mass, including: Marking the timing constraints of the current blasting operation, where the timing constraints correspond to a drilling operation window, a charging operation window, or a detonation operation window; Configuring an association rule base of the spatial position, the expansion trend, and the annotated temporal constraints in a fuzzy logic decision system, wherein the association rule base defines the allowable limit values ​​of the distance between the fissure position and the core area of ​​the blasting operation under different window periods; A collaborative analysis operation of the spatial position, the expansion trend and the annotated temporal constraints is performed in the association rule base to generate a fracture risk distribution map annotating the instantaneous stability state of the rock mass.

3. The method according to claim 1, characterized in that The method includes calculating the time difference data of sound wave propagation between adjacent sensor nodes based on the sound wave signal, and determining the spatial position of new cracks in the rock mass and the expansion trend of existing cracks according to the change characteristics of the time difference data, including: Calculating the propagation time difference of the acoustic wave signal received by each pair of adjacent nodes in the piezoelectric sensor array to form a time difference data set of multiple groups of node pairs; Extracting the fluctuation morphological features of the time difference data set during the blasting process, and identifying abnormal node pair combinations with sudden or continuous increase in time difference characteristics based on the fluctuation morphological features; According to the physical position association relationship and the time difference growth rate of the abnormal node pair combination, the spatial position point set of the new crack and the expansion trend of the existing crack are mapped and generated.

4. The method according to claim 1, wherein The generation of scheduling instructions based on the safety risk level, and the dynamic adjustment of subsequent drilling operation positions, charging operation sequence, and detonation network timing at the blasting engineering site through the scheduling instructions to optimize the on-site operation process and avoid potential rock instability risks, include: According to the safety risk level, a preset risk scheduling mapping rule is called to generate a scheduling instruction including a drilling position offset, a charging sequence adjustment list, and a detonation sequence delay value; executing the scheduling instructions at the blasting engineering site, correcting the drilling operation position according to the drilling position offset, reassembling the charging operation sequence according to the charging sequence adjustment list, and updating the detonation network sequence according to the detonation sequence delay value; Based on the revised drilling operation position, the reorganized charging operation sequence and the updated detonation network timing, an optimized operation process is constructed to avoid potential rock instability risks.

5. The method according to claim 1, wherein The instantaneous stability of the rock mass represented by the fracture risk distribution map is used to determine the safety risk level faced by the blasting engineering site. The safety risk level quantitatively represents the possibility of the rock mass becoming unstable during subsequent blasting operations, including: dividing the fracture risk distribution map into rock mass spatial grid units, and counting the instability probability level values ​​recorded by the rock mass spatial grid units; Calculating the dynamic impact factor of the rock mass spatial grid unit based on the advancing direction of the blasting operation, wherein the dynamic impact factor of the adjacent grid unit at the current construction position is higher than that of the distal grid unit; The instability probability level value and the dynamic impact factor are superimposed to generate a safety risk level faced by the blasting engineering site, and the safety risk level quantitatively represents the possibility of rock mass instability in subsequent blasting operations.

6. The method according to claim 3, characterized in that The step of extracting the fluctuation shape characteristics of the time difference data set during the blasting process and identifying abnormal node pairs with sudden or continuous increase in time difference characteristics according to the fluctuation shape characteristics includes: Collecting a time difference data set of each pair of adjacent nodes in the piezoelectric sensor array during the blasting drilling stage, the charging stage, and the detonation stage; Extracting a fluctuation rising rate value and a fluctuation continuous growth duration value of the time difference data set, wherein the fluctuation rising rate value represents the time difference growth amplitude per unit time, and the fluctuation continuous growth duration value represents the duration of the continuous growth state; Identify the node pair combination whose fluctuation rising rate value exceeds the sudden increase determination threshold and whose fluctuation continuous growth duration value exceeds the continuous determination threshold, and mark them as abnormal node pair combinations.

7. The method according to claim 2, characterized in that The performing of a collaborative analysis operation of the spatial position, the expansion trend, and the annotated temporal constraints in the association rule base to generate a fracture risk distribution map annotating the instantaneous stability state of the rock mass includes: Mark the timing constraints of the current blasting operation and generate marked timing constraints; Configuring an association rule base of the spatial position, the expansion trend, and the annotated temporal constraints, wherein the association rule base defines proximity limits between the crack position and the blasting core area under different window labels; A collaborative analysis operation of the spatial position, the expansion trend and the annotated temporal constraints in the association rule base is performed to generate a fracture risk distribution map annotating the instantaneous stability state of the rock mass.

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