Intelligent millisecond blasting effect evaluation and analysis method and system and medium

Abstract

The invention relates to the technical field of blasting effect evaluation, and discloses a millisecond blasting effect intelligent evaluation analysis method and system and a medium, and the method comprises the steps: arranging a multi-source sensor to collect vibration, displacement, hydraulic support stress and earth surface deformation data before and after blasting, and constructing a standardized blasting data set; calculating vibration amplitude variation based on stress wave propagation characteristics, and judging the crushing effect; evaluating the rock mass stability and the working face safety by combining the displacement increment and the mine pressure strength; a response parameter set is formed through multi-time-sequence node monitoring, monitoring partitions are divided, prediction and actual indexes are compared, and a verification result is generated; and feeding back a correction evaluation threshold value, and iteratively optimizing blasting effect evaluation. According to the invention, accurate, dynamic and closed-loop intelligent evaluation of the millisecond blasting effect can be realized.

Description

Technical Field

[0001] This invention relates to the field of blasting effect evaluation technology, and more specifically, to a method, system, and medium for intelligent evaluation and analysis of micro-delay blasting effects. Background Technology

[0002] With the continuous development of blasting technologies in mining, tunneling, and other engineering projects, especially in areas such as differential pressure blasting control, vibration suppression, and crushing quality optimization, the demand for high-precision and intelligent evaluation of blasting effects is becoming increasingly prominent. Currently, known methods for evaluating differential pressure blasting effects mainly rely on macroscopic indicators such as block size distribution statistics, over- and under-excavation calculations, and on-site observations. While these methods have achieved some success in engineering applications, the differential pressure blasting process involves complex temporal dynamics, and its blasting response is influenced by multiple heterogeneous factors, including vibration signals, rock mass parameters, and delay accuracy. Existing methods still have systematic shortcomings in areas such as temporal dynamic mechanism modeling, multi-source heterogeneous data fusion analysis, and quantitative evaluation of energy utilization efficiency and stress wave superposition effects. They cannot fully characterize the dynamic response characteristics under the coupling of multiple factors in differential pressure blasting, thus limiting the refined and intelligent evaluation of blasting effects.

[0003] Therefore, how to effectively integrate physical mechanisms and data-driven methods to build an intelligent analysis system for micro-delay blasting effects with the ability to analyze time-series features, process multi-modal information collaboratively, and conduct adaptive evaluation has become a pressing technical problem that needs to be solved. Summary of the Invention

[0004] This invention provides a method, system, and medium for intelligent evaluation and analysis of micro-delay blasting effects, which solves the technical problems in the prior art that cannot effectively integrate physical mechanisms and data-driven methods, and cannot fully characterize the dynamic response characteristics under the coupling effect of multiple factors, thus limiting the refined and intelligent evaluation of blasting effects.

[0005] This invention provides an intelligent evaluation and analysis method, system, and medium for micro-delay blasting effects, including: Firstly, a method for intelligent evaluation and analysis of micro-delay blasting effects includes: Multi-source monitoring data from micro-delay blasting sites were collected and preprocessed with time-series alignment and normalization to construct a unified standard blasting dataset. Extract the superposition intensity, energy coupling level, vibration attenuation law, propagation path, and physical characteristics of stress wave propagation by porous time-delay interference from the blasting dataset. By utilizing the physical characteristics of stress wave propagation, the blasting dataset is analyzed from multiple angles to comprehensively assess the blasting fracturing effect, rock mass stability, and mine pressure risk, thereby obtaining evaluation results. Specifically, the multi-angle collaborative analysis includes: normalizing the vibration amplitude change ΔV, displacement increment ΔD, and mine pressure intensity P, and then weighting and summing them according to weights w1, w2, and w3 to obtain the comprehensive evaluation index E=w1·ΔV+w2·ΔD+w3·P, where the weights w1, w2, and w3 are dynamically adjusted according to the rock mass integrity coefficient Kv. When Kv>0.75, w1=0.5, w2=0.3, w3=0.2; when 0.5≤Kv≤0.75, w1=0.4, w2=0.4, w3=0.2; when Kv<0.5, w1=0.3, w2=0.5, w3=0.2. Based on the evaluation results, various blasting response parameters, including initial blasting response, secondary fracturing, mining-induced damage, surface deformation, and changes in mine pressure, were collected at different time points. These parameters were then chained together in chronological order to construct a response parameter set containing temporal relationships. The actual blasting time sequence indicators are extracted from the response parameter set and combined with spatial location information to divide the blasting area into multiple monitoring zones with different response characteristics. The deviation rate is calculated by comparing the actual blasting effect indicators of each monitoring zone with the predicted indicators in the evaluation results item by item to obtain the verification results of the blasting effect. The verification results are fed back to the evaluation result generation step to iteratively generate optimized blasting evaluation results.

[0006] Furthermore, multi-source monitoring data were collected at the micro-delay blasting site, including: Multiple vibration sensors are deployed around the blasting area to record the vibration reference value and location coordinates of each sensor before the blast. A spatial relationship matrix is ​​constructed based on the positional relationship between the sensors and the blasting point. The influence range of the stress wave is calculated based on the spatial relationship matrix. Holes are drilled in the rock mass surrounding the influence range and displacement monitoring equipment is installed. The displacement reference value of each monitoring point before blasting is recorded to obtain displacement reference data. The spatial relationship matrix is ​​combined with the displacement reference data to determine the key blasting monitoring area. The surface deformation of the key blasting monitoring area is monitored using surface remote sensing equipment to obtain surface deformation characteristic data. The working resistance value and column pressure value of each hydraulic support are collected in real time on the hydraulic support of the working face corresponding to the blasting area, and the stress change of the hydraulic support before and after the blasting is recorded to obtain the stress data of the hydraulic support. High-sensitivity vibration frequency monitoring instruments are deployed at key locations in mine roadways to continuously monitor the vibration frequency of rock mass caused by blasting, record the spectral distribution characteristics and main frequency variation patterns of the vibration frequency, and obtain vibration frequency data within the mine. The vibration reference value, the displacement reference value, the surface deformation characteristic data, the hydraulic support force data, and the mine vibration frequency data are integrated to form a blasting dataset.

[0007] Furthermore, based on the physical characteristics of stress wave propagation, a multimodal collaborative evaluation is performed on the blasting dataset to obtain evaluation results, including: The physical characteristics of stress wave propagation specifically include the intensity of the superposition of stress waves generated by multiple blasting holes, the level of coupling between blasting energy and rock mass, the law of vibration intensity attenuation with distance, the propagation path of stress waves in rock mass, and the waveform interference characteristics caused by the delay of different blasting holes. A calculation model for the change in vibration amplitude is established based on the physical characteristics of stress wave propagation. The average value of vibration data measured at each monitoring point after blasting is subtracted from the average value of the vibration reference value before blasting to obtain the actual change in vibration amplitude. In the stress wave superposition region, any vibration sensor is selected as the reference monitoring point, and the target vibration amplitude change threshold to be achieved for the reference monitoring point is set according to the energy coupling requirements of micro-differential blasting. The actual vibration amplitude change at the benchmark monitoring point is compared with the target vibration amplitude change threshold to obtain the analysis results of the blasting and breaking effect. Specifically, when the actual vibration amplitude change at the benchmark monitoring point reaches or exceeds the target vibration amplitude change threshold, it indicates that the stress wave superposition is sufficient and the energy coupling is good, and the blasting effect is judged to have reached the complete fragmentation standard; when the actual vibration amplitude change at the benchmark monitoring point is lower than the target vibration amplitude change threshold, it indicates that the stress wave superposition is insufficient, and the blasting effect is judged to have only reached the local fragmentation standard. Based on the analysis results, a critical displacement threshold for rock mass structure instability is set. When the rock mass is determined to be completely broken, the critical displacement threshold is set to 1.2 to 1.5 times the pre-blasting displacement reference value to identify abnormal deformation. When the rock mass is determined to be partially broken, the critical displacement threshold is set to 2.0 to 3.0 times the pre-blasting displacement reference value to accommodate subsequent fracturing. Based on the critical displacement threshold for rock mass instability, the evaluation results of rock mass structure stability are obtained; Based on the fusion analysis of hydraulic support working resistance data and vibration spectrum data, the dynamic mine pressure manifestation intensity when the working face passes through the blasting zone is analyzed to obtain the evaluation result of mine pressure manifestation risk. The analysis results, assessment results, and evaluation results are integrated to form a comprehensive evaluation result that includes three dimensions: crushing effect, structural stability, and mine pressure risk.

[0008] Furthermore, the rock mass structure stability assessment results include: when the analysis results determine that the standard is localized breakage, reading the displacement monitoring values ​​of each monitoring point after blasting, subtracting the initial displacement reference value before blasting from the displacement value after blasting, and calculating the displacement increment of each monitoring point; The displacement increment of each monitoring point is compared with the critical displacement threshold for rock mass instability. When the displacement increment of any monitoring point exceeds the critical threshold, it is determined that the rock mass in the current monitoring area has collapsed, that is, the assessment result is unstable. When the displacement increment of all monitoring points does not exceed the critical threshold, it is determined that the rock mass structure is stable, that is, the rock mass structure stability assessment result is stable. The evaluation results include: setting a safety threshold for mine pressure intensity based on the safety requirement of fully releasing roof energy; comparing the mine pressure intensity obtained from comprehensive analysis with the safety threshold for mine pressure intensity; when the mine pressure intensity is lower than or equal to the safety threshold, it is determined that the working face can safely pass through the blasting area; when the mine pressure intensity exceeds the safety threshold, it is determined that there is a strong mine pressure risk, and the working face cannot pass directly and pressure relief measures must be taken.

[0009] Furthermore, based on the evaluation results, various data are obtained and time-series chained together to construct a response parameter set, including: Based on the comprehensive evaluation results, multiple time nodes are set for continuous monitoring, including an immediate monitoring node when blasting is completed, a delayed monitoring node after a certain period of time after blasting, and a monitoring node for the impact of mining when the working face advances to a preset distance threshold. When the blasting is completed, the dynamic response signals output by all vibration sensors are collected at the first real-time monitoring node. The peak value of the dynamic response signal is subtracted from the reference value of the initial vibration signal before the blast to calculate the actual vibration amplitude change corresponding to each sensor, thus forming the initial blast response data. When the comprehensive evaluation result is determined to be a local fracture standard, it means that the rock mass will undergo secondary fracture. Then, the vibration response signal of the benchmark monitoring point is collected again at the delayed monitoring node after a preset time interval. If the actual vibration amplitude change reaches or exceeds the target vibration amplitude change threshold at this time, it is determined that secondary fracture has occurred, and secondary fracture monitoring data is recorded. When the comprehensive evaluation result determines that the rock mass structure remains stable, it is determined whether the mining impact during the working face advance has triggered premature collapse. When the mining impact monitoring node at the preset distance of the working face advances, the current displacement value of each monitoring point is read, and the cumulative displacement increment from blasting to the current moment is calculated. If the cumulative displacement increment exceeds the critical displacement threshold for rock mass structure instability, it is determined that premature collapse has been triggered, and the premature collapse data is recorded. When the comprehensive evaluation results determine that the working face cannot be directly passed, the mine pressure release process is monitored. The working face advance is suspended and the changes in mine pressure intensity are continuously monitored until the mine pressure intensity is reduced to below the mine pressure intensity safety threshold, and the dynamic changes in mine pressure are recorded. When tension cracks extending along the direction of the working face or settlement troughs are detected on the ground surface, it is determined that premature damage has occurred on the ground surface, and the data on the evolution of ground surface deformation are recorded. The initial blasting response data, the secondary fracturing monitoring data, the mining-induced damage data, the surface deformation evolution data, and the dynamic change data of mine pressure are linked and associated according to their respective time sequence to establish the temporal relationship between the data and construct a response parameter set.

[0010] Furthermore, the temporal chain association includes: A three-layer anomaly detection architecture is constructed using the multiple time-series monitoring nodes to perform hierarchical anomaly detection during the blasting execution process and subsequent mining impact stages. The first layer of detection uses a physical rule engine to match real-time monitoring data with multiple preset blasting anomaly rules, screen out dynamic behaviors that do not conform to the normal blasting response pattern, and identify abnormal blasting events. The second layer of detection performs correlation analysis on the abnormal blasting events and calculates risk scores. When there are two or more abnormal blasting events, the risk scores are graded and assessed according to multiple risk level assessment threshold ranges, and the corresponding level of early warning mechanism is triggered in sequence to form a graded early warning from low to high. Specifically, the risk score S = Σ(λk·Ik), where Ik is the indicator variable for the k-th type of abnormal event (1 if it occurs, 0 otherwise), λk is its risk weight, λvibration exceeding the limit = 1.0, λdisplacement sudden increase = 1.2, λstent unloading = 0.8; when S ≥ 2.0, a first-level warning is triggered, and when 1.0 ≤ S < 2.0, a second-level warning is triggered. The third-layer detection dynamically adjusts the warning threshold range and monitoring time interval based on the feedback from the graded warnings to accurately define the risk level boundaries. In the third-layer detection, if N consecutive (N≥3) warnings of the same type are verified as false alarms, the corresponding λk is multiplied by a decay factor of 0.9, and the monitoring time interval for this type of event is extended by 20%. The abnormal blasting event is associated with the initial blasting response data, the secondary fracturing monitoring data, the mining-induced damage data, the surface deformation evolution data, and the dynamic change data of the mine pressure in chronological order of occurrence. Each data point is then marked with a timestamp and stored in the blasting process response database. An intelligent diagnostic mechanism is constructed based on the response database. The intelligent diagnostic mechanism is driven by the physical mechanism of stress wave propagation. When an abnormal blasting event is detected, a traceability query program is triggered to locate the dynamic cause of the abnormality by analyzing the data. Based on the historical data stored in the response database and the analytical experience accumulated by the intelligent diagnostic mechanism, a knowledge graph of blasting effect evolution is established. The knowledge graph can identify time-series correlation patterns including the propagation path of stress waves during the initial blast, the spatial distribution of the insufficiently broken area, the triggering conditions of secondary fracturing, the advance range of mining impact, and the evolution law of mine pressure manifestation. The evolutionary knowledge graph quantifies the dynamic response characteristics of blasting effects throughout the entire process, from initial detonation, rock mass response, secondary fracturing, mining impact, to safe passage of the working face, forming a temporal chain-like correlation system of mutual influence among each stage.

[0011] Furthermore, the blasting area is intelligently spatially partitioned according to the set of response parameters to form multiple monitoring zones, including: Three core indicators—stress wave superposition intensity, rock mass deformation response characteristics, and mining impact range—are extracted from the response parameter set. Combined with the spatial location information of sensor distribution location, spatial gradient change, deformation distribution range, surface impact area, and support stress distribution, the blasting area is divided into multiple monitoring zones with different response characteristics. The multiple monitoring zones are dynamically tracked. By analyzing the historical blasting data of each monitoring zone, evaluation units that exhibit stress wave superposition trends, frequent secondary fracture trends, stable rock mass structure trends, high incidence of mining-induced collapse trends, and abnormal mine pressure manifestation trends are identified. The development trend characteristics of the evaluation unit are correlated with the physical characteristics of stress wave propagation to obtain the mutual influence relationship between different monitoring zones and establish a response mechanism describing the coupling effect of blasting effect and mining behavior. Based on the aforementioned response mechanism, a spatial correlation matrix is ​​constructed. This spatial correlation matrix quantitatively characterizes the propagation path of stress waves in space, the attenuation law of energy with time and space, the spatial evolution characteristics of rock mass deformation, the spatial distribution law of secondary fracturing, and the spatial range of the impact of mining advance. Based on the mutual influence relationships in the spatial correlation matrix, a cross-regional dynamic response linkage mechanism is established. Through this linkage mechanism, the state change of any evaluation unit can automatically trigger the coordinated response of its associated evaluation units, thereby achieving coordinated monitoring of the main blasting area and the auxiliary monitoring area.

[0012] Furthermore, the actual blasting time sequence indicators of each monitoring zone are collected and compared with the blasting effect indicators in the evaluation results to obtain the verification results of the blasting effect, including: Collect actual blasting timing indicators for each monitoring zone; The blasting timing indicators include quantitative indicators such as the actual vibration amplitude change, the intensity of the stress wave superposition effect, the trigger time of secondary fracturing, the rock mass displacement increment, the surface deformation characteristics, the intensity of the mining pressure manifestation, the safe passage time of the working face, and the scope of the mining advance influence. The actual blasting timing index is compared with the blasting effect index in the evaluation results item by item, and the relative deviation rate is calculated. The formula for calculating the relative deviation rate is: Relative deviation rate = |actual value - predicted value| / actual value × 100%; A preset comparison threshold is set. When the relative deviation rate is less than or equal to the preset comparison threshold, the evaluation result is determined to be accurate and reliable, and the monitoring partition is marked as a verification-passed partition. When the relative deviation rate is greater than the preset comparison threshold, it is determined that the evaluation result has a deviation, the monitoring zone is marked as a deviation zone, and the geological conditions, blasting parameters and monitoring data of the deviation zone are extracted as abnormal samples. The number of verified areas in each monitoring zone is counted, and the abnormal samples are summarized to form a deviation dataset, resulting in a blasting effect verification result that includes verified zones, deviation zones, and deviation datasets.

[0013] Secondly, a smart evaluation and analysis system for micro-delay blasting effects includes: Data acquisition module: used to collect multi-source monitoring data from micro-delay blasting sites, and perform time-series alignment and normalization preprocessing to construct a unified standard blasting dataset; Effect evaluation module: used to extract stress wave propagation physical characteristics based on the blasting dataset; use the stress wave propagation physical characteristics to perform multi-angle collaborative analysis on the blasting dataset, comprehensively judge the blasting fracturing effect, rock mass stability and mine pressure risk, and obtain evaluation results; The correlation module is used to collect various blasting response parameters at different time points based on the evaluation results, including initial blasting response, secondary fracturing, mining-induced damage, surface deformation, and changes in mine pressure. It performs chain correlation processing on various parameters in chronological order to construct a response parameter set containing temporal relationships. The zone monitoring module is used to extract actual blasting time sequence indicators from the response parameter set and combine them with spatial location information to divide the blasting area into multiple monitoring zones with different response characteristics. Verification and optimization module: This module is used to compare the actual blasting effect indicators of each monitoring zone with the predicted indicators in the evaluation results item by item to calculate the deviation rate and obtain the verification results of the blasting effect; the verification results are fed back to the evaluation result generation step to iteratively generate the optimized blasting evaluation results.

[0014] Thirdly, a computer-readable storage medium is characterized in that it is used to store computer-readable instructions, which, when read by a computer, enable the execution of the intelligent evaluation and analysis method for micro-delay blasting effects.

[0015] The beneficial effects of this invention are as follows: By collecting and standardizing various types of monitoring data at the micro-delay blasting site, and combining this data with the physical characteristics of stress wave propagation for multi-angle collaborative analysis, this invention effectively integrates physical mechanisms and data-driven methods, overcoming the limitations of existing technologies that rely solely on macroscopic indicators for evaluation. By collecting multiple types of blasting response parameters at different time points and constructing a time-series response parameter set, and combining this with spatial location information for monitoring zoning, this invention achieves collaborative analysis in both time and spatial dimensions, fully characterizing the dynamic response characteristics under the combined influence of multiple heterogeneous factors such as vibration signals, rock mass parameters, and delay accuracy. This compensates for the systematic deficiencies of existing methods in modeling time-series dynamic mechanisms and fusing heterogeneous data. By extracting physical characteristics such as the superposition intensity and energy coupling level of stress waves and establishing a calculation model, this invention achieves accurate quantitative evaluation of energy utilization efficiency and the superposition effect of stress waves. By comparing and verifying actual blasting effect indicators with predicted indicators, and feeding deviation information back to the evaluation model to automatically adjust parameters, this invention constructs a closed-loop evaluation system with adaptive capabilities. This allows the evaluation system to continuously improve its evaluation accuracy with the accumulation of application scenarios, achieving refined and intelligent evaluation of micro-delay blasting effects. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a method for intelligent evaluation and analysis of micro-delay blasting effects provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a micro-delay blasting effect intelligent evaluation and analysis system module provided in an embodiment of the present invention. Detailed Implementation

[0017] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0018] At least one embodiment of the present invention discloses a method, system, and medium for intelligent evaluation and analysis of micro-delay blasting effects, including: like Figure 1 As shown, a method for intelligent evaluation and analysis of micro-delay blasting effects includes the following steps: Step 1: Collect multi-source monitoring data from the micro-delay blasting site, and perform time-series alignment and normalization preprocessing to construct a unified standard blasting dataset; Step 2: Extract the physical characteristics of stress wave propagation based on the blasting dataset; use the physical characteristics of stress wave propagation to perform multi-angle collaborative analysis on the blasting dataset, comprehensively judge the blasting fracturing effect, rock mass stability and mine pressure risk, and obtain evaluation results; Step 3: Based on the evaluation results, collect various blasting response parameters at different time points, including initial blasting response, secondary fracturing, mining-induced damage, surface deformation, and changes in mine pressure. Perform chain-linked processing on various parameters in chronological order to construct a response parameter set containing temporal relationships. Step 4: Extract the actual blasting time sequence indicators from the response parameter set and combine them with spatial location information to divide the blasting area into multiple monitoring zones with different response characteristics; Step 5: Compare the actual blasting effect indicators of each monitoring zone with the predicted indicators in the evaluation results to calculate the deviation rate and obtain the verification results of the blasting effect; feed the verification results back to the evaluation result generation step to iteratively generate the optimized blasting evaluation results.

[0019] The specific implementation method is as follows: Before carrying out micro-delay blasting operations at the underground mining face of a metal mine, multiple vibration sensors are first deployed around the blasting area. The vibration sensors are triaxial accelerometers, model PCB352C33, with a range of ±500g and a sampling frequency of 10kHz. They are installed on the rock surface within a horizontal distance of 5m to 30m from the center of the blast hole. Each sensor is connected to the central data acquisition unit via a waterproof aviation connector and shielded twisted-pair cable. Wired transmission is used for communication to ensure a high signal-to-noise ratio. Simultaneously, all vibration sensors are equipped with GPS timing modules, achieving time synchronization accuracy better than ±1ms. After deployment, the vibration signals collected by each sensor for 10 consecutive minutes before blasting are recorded. The root mean square value is taken as the vibration reference value for that point. A total station is used to determine the three-dimensional spatial coordinates (X, Y, Z) of each sensor in a unified coordinate system within the mining area, forming a data table containing location information and reference vibration values.

[0020] Based on the spatial geometric relationship between each vibration sensor and the blasting hole, a spatial relationship matrix M is constructed, where the matrix element mij represents the Euclidean distance and direction cosine between the i-th vibration sensor and the j-th blasting hole, used for subsequent stress wave propagation path modeling. Based on this spatial relationship matrix M, combined with the longitudinal wave velocity (measured at 4200 m / s) and transverse wave velocity (measured at 2400 m / s) of the rock mass, the influence range of stress wave propagation in the rock mass is calculated. This range is defined as the area extending outward from the farthest blasting hole at a distance of 1.5 times the maximum charge radius. Within the intact rock mass surrounding the affected area, six monitoring holes were drilled perpendicular to the tunnel's direction, with a depth of 8m, a diameter of 50mm, and a spacing of 3m, arranged in a linear pattern. Each monitoring hole was equipped with a displacement monitoring device consisting of a stainless steel sleeve, a displacement probe, and signal leads. The displacement probe was a differential transformer type displacement sensor (LVDT) with a range of ±10mm and an accuracy of 0.1%. Its sensing end was fixed to the contact surface between the monitoring hole wall and the surrounding rock using epoxy resin adhesive to ensure synchronization with rock mass deformation. All signal cables from the displacement monitoring devices were collected in an explosion-proof junction box and then connected to the central data acquisition unit via armored cables to complete the electrical connection. Before blasting, displacement data was continuously collected from each monitoring point for 30 minutes, and the average value was used as the initial displacement reference value.

[0021] Simultaneously, surface remote sensing equipment is used to continuously scan the key blasting monitoring area determined by the spatial relationship matrix M and displacement reference data. The surface remote sensing equipment is a synthetic aperture radar (SAR) system, installed at a high point on the mining area surface, operating at a frequency of 9.6 GHz, with a repeat observation cycle of 1 hour and a spatial resolution of 0.5 m. It acquires surface deformation characteristic data, including surface subsidence rate, horizontal displacement vector, and deformation gradient field, using interferometry technology. All data is transmitted in real time to the storage server of the central data acquisition unit.

[0022] A hydraulic support monitoring module is installed on the working face support structure corresponding to the blasting area. This module is integrated inside the column of the ZY10800 / 28 / 60 hydraulic support and contains two pressure sensors: one installed in the lower cavity of the column to monitor working resistance, and the other installed in the upper cavity of the column to monitor column pressure. The sensor model is HDA4744-B-400-000, with a range of 0–40MPa and an output signal of 4–20mA standard current signal. The data logger uses an industrial-grade embedded controller with local SD card storage and 4G wireless upload capabilities, and the sampling frequency is set to 10Hz. Continuous recording begins 24 hours before blasting, acquiring hydraulic support stress change data for at least 48 hours before and after blasting, including peak working resistance, pressure fluctuation frequency, and unloading response time.

[0023] High-sensitivity vibration frequency monitoring instruments were deployed at key cross-sections of the mine roadways, specifically at the intersection of the return airway and transport roadway closest to the blasting zone. These instruments were fiber Bragg grating (FBG) vibration sensor arrays, comprising 12 sensing points encapsulated in Φ8mm stainless steel protective tubes and anchored to the roadway roof strata with epoxy resin. The sensor signals were transmitted via single-mode optical fiber to a demodulator (SM130 model) with a sampling rate of 1kHz and a wavelength resolution of 1pm. All vibration frequency monitoring instruments were connected to a central data acquisition unit via fiber optic links, enabling low-latency, electromagnetic interference-resistant data transmission. During blasting, the spectral distribution characteristics of the rock mass vibration signals were continuously recorded, including the dominant frequency, harmonic components, bandwidth, and energy concentration, and the curve of the dominant frequency changing over time was extracted.

[0024] After completing the installation and data acquisition preparation of all the aforementioned monitoring equipment, the vibration reference values, initial displacement reference values, surface deformation characteristic data, hydraulic support force change data, and vibration frequency data are uniformly imported into the data preprocessing module. This module runs on the industrial control computer of the central data acquisition unit, using a data processing script written in Python 3.9. First, all data streams are timestamped, using GPS timing signals as a reference to synchronize data from different sources to the same time axis, with the time alignment error controlled within ±2ms. Subsequently, each physical quantity is normalized: vibration amplitude is normalized to the 0–1 range, displacement is normalized to the relative change rate of the initial reference value, and pressure values ​​are converted into dimensionless safety factors. Finally, a standardized blasting dataset is formed and stored in an SQLite database for subsequent analysis.

[0025] Subsequently, a micro-delay blasting operation was carried out, employing an electronic detonator initiation system with three design delay times of 15ms, 25ms, and 40ms. The total charge was 120kg, and the blasting holes were arranged in a fan-shaped cutout pattern, with a hole depth of 3.2m and a hole spacing of 1.0m. After the blasting, the superposition intensity of stress waves, energy coupling level, vibration attenuation law, propagation path, and waveform interference characteristics formed by multi-hole delay interference were extracted from the blasting dataset. Specifically, wavelet transform was used to perform time-frequency analysis on the vibration signal to identify the time difference of stress waves from different blasting holes reaching each sensor, and the energy density of the superposition area was calculated. The phase relationship of the waveform interference was determined using a cross-correlation function, thereby quantifying the interference enhancement or cancellation effect.

[0026] Based on the physical characteristics of stress wave propagation, a calculation model for the change in vibration amplitude is established. This model defines the actual change in vibration amplitude as the average vibration data at each monitoring point after blasting minus the average vibration baseline value before blasting. In the region of significant stress wave superposition, i.e., the wavefront convergence zone calculated from the spatial relationship matrix M, vibration sensor numbered V-07 is selected as the baseline monitoring point. This point is located at the equivalent center of action of the two sets of delayed blasting holes, at distances of 12.3m and 13.8m, respectively. According to the micro-delay blasting design parameters, the target vibration amplitude change threshold Tv is derived from the uniaxial compressive strength Rc of the rock mass, the blasting hole spacing d, and the charge density ρe through statistical regression from historical blasting tests: Tv = k·(Rc). α ·(d) β ·(ρe) γ It is determined that k, α, β, and γ are coefficients calibrated through historical blasting tests. Therefore, the threshold value of the target vibration amplitude change corresponding to this benchmark monitoring point is set to 0.85 mm / s, which corresponds to the minimum energy input required for complete breakage.

[0027] The actual vibration amplitude change at the benchmark monitoring point V-07 (measured at 0.92 mm / s) was compared with the target threshold of 0.85 mm / s. Since 0.92 ≥ 0.85, the blasting and breaking effect was determined to have reached the standard of complete breaking.

[0028] Based on the determination result, in step S004, the critical displacement threshold for rock mass structure instability is set to 1.35 times the initial displacement reference value (taking an intermediate value between 1.2 and 1.5 times). The initial displacement reference values ​​for each monitoring hole are D1=0.12mm, D2=0.09mm, D3=0.15mm, D4=0.11mm, D5=0.13mm, and D6=0.10mm, respectively, with corresponding critical displacement thresholds of 0.162mm, 0.122mm, 0.203mm, 0.149mm, 0.176mm, and 0.135mm. Within 10 minutes after the blast, the displacement monitoring values ​​of each monitoring point were read as follows: 0.14 mm, 0.11 mm, 0.18 mm, 0.13 mm, 0.16 mm, and 0.12 mm, respectively. The calculated displacement increments were 0.02 mm, 0.02 mm, 0.03 mm, 0.02 mm, 0.03 mm, and 0.02 mm, respectively. None of them exceeded their respective critical values, so the rock mass structure was determined to be stable.

[0029] Simultaneously integrating the hydraulic support's working resistance data and vibration spectrum data: the maximum working resistance of the hydraulic support within 30 minutes after blasting was 9800kN, lower than its rated working resistance of 10800kN; the vibration spectrum showed that the main frequency was concentrated in 35–45Hz, with no obvious low-frequency energy accumulation. Based on this, the dynamic mine pressure manifestation intensity was calculated to be 0.82 (a dimensionless index, defined as the ratio of measured resistance to rated resistance multiplied by the low-frequency energy proportion correction factor in the spectrum). The safe threshold for mine pressure intensity was set at 0.85. Since 0.82 ≤ 0.85, it was determined that the working face could safely pass through the blasting area.

[0030] In step S005, blasting response parameters are continuously collected at multiple preset time points: At the real-time monitoring node (0–5 minutes after blasting), all vibration sensor signals are collected, and the actual vibration amplitude change at each point is calculated to form initial blasting response data; because the comprehensive evaluation indicates complete fracture and stable rock mass, at the mining impact monitoring node (when the working face advances to 15m from the blasting zone), the current displacement value of each monitoring point is read, and the cumulative displacement increment is calculated. The results are 0.05mm, 0.04mm, 0.06mm, 0.05mm, 0.07mm, and 0.04mm, respectively, still below the critical value, and no premature collapse record is triggered; the surface remote sensing equipment did not detect any tension cracks or settlement troughs extending along the working face advancement direction within 24 hours after blasting, therefore no surface deformation evolution data is generated; all collected data are chained together in chronological order to construct a response parameter set including timestamps, spatial coordinates, physical quantity values, and judgment labels.

[0031] In step S006, actual blasting time-series indicators are extracted from the response parameter set, including actual vibration amplitude change of 0.92 mm / s, stress wave superposition effect intensity index of 1.28 (calculated based on energy superposition ratio), no secondary fracturing trigger time, maximum rock mass displacement increment of 0.03 mm, no abnormal surface deformation characteristics, mine pressure manifestation intensity of 0.82, safe passage time of working face of 4.2 hours, and mining advance influence range of 12 m. The blasting area is divided into three monitoring zones: Zone A (near blast zone, 0–10 m), Zone B (medium blast zone, 10–20 m), and Zone C (far blast zone, 20–30 m). The prediction indicators for each zone are derived from simulation results of the blasting design software. For example, zone A predicts a vibration amplitude change of 0.90 mm / s, while the actual measured value is 0.92 mm / s, resulting in a relative deviation rate of |0.92–0.90| / 0.92×100%≈2.17%. A preset comparison threshold of 5% is set; since 2.17% ≤ 5%, zone A is marked as a verified zone. Similarly, the relative deviation rates for zones B and C are 3.8% and 4.5% respectively, both within the limit, and both are marked as verified zones without generating any abnormal samples.

[0032] Finally, in step S007, the verification results are fed back to the comprehensive evaluation result generation step in S003. Since all zones passed verification, only the target vibration amplitude change threshold is fine-tuned, corrected from 0.85 mm / s to 0.84 mm / s, the critical displacement threshold coefficient is adjusted from 1.35 to 1.33, and the mine pressure strength safety threshold remains unchanged at 0.85. The optimized blasting evaluation results are generated iteratively to guide the design of the next blasting cycle. Throughout the process, the connection relationships, spatial layout, data flow, and judgment logic of all equipment strictly follow the aforementioned technical scheme to ensure the accuracy and repeatability of the evaluation results.

[0033] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principles of this invention are further supplemented below with a specific application scenario.

[0034] Before conducting micro-delay blasting operations at an underground mining face in a metal mine, the spatial coordinates of the blast holes and the delayed detonation sequence were first determined based on the blasting design drawings. Based on this, several stable rock surface points were selected within a 5m to 30m radius of the blasting area, and triaxial accelerometers were installed as vibration sensors. These vibration sensors were connected to a central data acquisition unit via a waterproof aviation connector and shielded twisted-pair cable, employing wired transmission to suppress the influence of strong electromagnetic interference underground. Simultaneously, each sensor integrated a GPS timing module to ensure that the time error of multi-point synchronous sampling was controlled within ±1ms. Before the formal detonation, the system continuously collected environmental vibration signals for 10 minutes, calculated their root mean square value as the vibration reference value for that point, and used a total station to determine the three-dimensional spatial coordinates (X, Y, Z) of each sensor in a unified coordinate system within the mining area, forming a data table containing location information and reference vibration values, providing a geometric basis for subsequent stress wave propagation path modeling.

[0035] Subsequently, based on the spatial geometric relationship between each vibration sensor and each blasting hole, a spatial relationship matrix M is constructed, where the matrix element mij is composed of the Euclidean distance and its direction cosine between the i-th sensor and the j-th blasting hole. The spatial relationship matrix M is derived from the theoretical arrival time tij = Lij / vp of the stress wave received by the i-th sensor from the j-th blasting hole, where Lij is the Euclidean distance in mij, and vp is the longitudinal wave velocity of the rock mass along the direction indicated by the direction cosine in mij. vp is calculated by combining core acoustic wave testing with the direction cosine and the rock mass anisotropy tensor. This matrix characterizes the path characteristics of stress wave propagation from each blasting source to each monitoring point. Combining the field-measured longitudinal wave velocity of 4200 m / s and transverse wave velocity of 2400 m / s, the effective influence range of the stress wave in the rock mass is calculated. This range is defined as the distance extending outward from the center of the farthest blasting hole by 1.5 times the maximum charge radius. Within the intact surrounding rock outside the affected area, six monitoring boreholes, each 8m deep and 50mm in diameter, with a spacing of 3m, were drilled linearly along a direction perpendicular to the tunnel's direction. Each borehole was equipped with a displacement monitoring device consisting of a stainless steel casing, an LVDT displacement probe, and signal leads. The LVDT probe's sensing end was firmly bonded to the contact surface between the borehole wall and the surrounding rock using epoxy resin, ensuring accurate reflection of the surrounding rock's radial deformation. All signal lines were collected via an explosion-proof junction box and connected to a central data acquisition unit via armored cables. Before blasting, the system continuously collected displacement data for 30 minutes, and the average value was used as the initial displacement benchmark for establishing subsequent rock mass stability criteria.

[0036] Meanwhile, a synthetic aperture radar (SAR) system, erected at a high point on the surface of the mining area, periodically scans the key monitoring area delineated by the spatial relationship matrix M and displacement reference data. This SAR system operates at a frequency of 9.6 GHz, has a repeat observation period of 1 hour, and a spatial resolution of 0.5 m. It acquires surface deformation characteristic data, including subsidence rate, horizontal displacement vector, and deformation gradient field, using differential interferometry, and transmits the data in real time to the storage server of the central data acquisition unit for assessing the potential disturbance of the blasting to the surface structure.

[0037] In the working face support structure corresponding to the blasting area, a hydraulic support monitoring module is integrated inside the ZY10800 / 28 / 60 hydraulic support column. A pressure sensor is installed in the lower cavity of the column to monitor working resistance, and another pressure sensor is installed in the upper cavity to monitor the internal pressure of the column. Both sensors are model HDA4744-B-400-000, outputting a standard 4–20mA current signal. The data logger uses an industrial-grade embedded controller with local SD card storage and 4G wireless upload capabilities, and the sampling frequency is set to 10Hz. The system continuously records data starting 24 hours before blasting, acquiring the dynamic stress of the hydraulic support for at least 48 hours before and after blasting, including peak working resistance, pressure fluctuation frequency, and unloading response time, providing support for assessing the intensity of mine pressure manifestation.

[0038] An array of fiber Bragg grating (FBG) vibration sensors was anchored in the roof strata of the roadway at the intersection of the return airway and the transport roadway, closest to the blasting zone. This array, comprising 12 sensing points encapsulated in an 8mm stainless steel protective tube and fixed to the borehole wall with epoxy resin, transmits the sensor signals via single-mode fiber to a wavelength division multiplexer (WDM), and then to an SM130 demodulator, achieving high-precision spectral acquisition with a 1kHz sampling rate and 1pm wavelength resolution. During blasting, the system continuously records the spectral distribution characteristics of the rock mass vibration signals, including the dominant frequency, harmonic components, bandwidth, and energy concentration, and extracts the curve of the dominant frequency's evolution over time to identify potential instability signs such as low-frequency energy accumulation.

[0039] After deploying the aforementioned multi-source monitoring equipment, the vibration reference values, initial displacement reference values, surface deformation characteristic data, hydraulic support force change data, and vibration frequency data are uniformly imported into the data preprocessing module running on the central data acquisition unit's industrial control computer. This module uses a data processing script written in Python 3.9. First, using GPS timing signals as a reference, it timestamps all heterogeneous data streams to ensure that the time synchronization error does not exceed ±2ms. Then, it normalizes each physical quantity: vibration amplitude is mapped to the 0–1 interval, displacement is converted into a rate of change relative to the initial reference value, and pressure values ​​are converted into a dimensionless safety factor. Finally, a structured and standardized blasting dataset is formed and stored in an SQLite database for subsequent analysis.

[0040] During micro-delay blasting operations, an electronic detonator initiation system was used, with detonation following a three-stage delay sequence of 15ms, 25ms, and 40ms. The total charge was 120kg, and the blasting holes were arranged in a fan-shaped slotted pattern, with a hole depth of 3.2m and a hole spacing of 1.0m. After blasting, vibration signals recorded by each sensor were extracted from the database. Time-frequency analysis was performed using continuous wavelet transform to identify the time difference of stress waves from different blasting holes reaching each monitoring point, and the energy density of the wavefront convergence region was calculated accordingly. Simultaneously, the phase relationship of the multi-hole blasting signals was analyzed using cross-correlation functions to quantify whether waveform interference enhanced or canceled, thereby obtaining the stress wave superposition effect intensity index.

[0041] Based on stress wave propagation theory, a calculation model for vibration amplitude change was established, defined as the average vibration data of each monitoring point after blasting minus the average vibration baseline value before blasting. In the area of ​​significant stress wave superposition identified by the spatial relationship matrix M, sensor V-07, located at the equivalent center of action of the two sets of delayed blasting holes, was selected as the baseline monitoring point, at distances of 12.3m and 13.8m from the two blasting holes, respectively. Based on regression analysis of historical blasting tests, the target vibration amplitude change threshold at this point was set to 0.85mm / s, corresponding to the minimum energy input required for complete fragmentation. The measured vibration amplitude change at point V-07 was 0.92mm / s. Since 0.92 ≥ 0.85, the blasting fragmentation effect was deemed to have reached the standard for complete fragmentation.

[0042] Based on this determination, in step S004, the critical displacement threshold for rock mass instability is set to 1.35 times the initial displacement reference value. The initial displacement reference values ​​for each monitoring hole are D1=0.12mm, D2=0.09mm, D3=0.15mm, D4=0.11mm, D5=0.13mm, and D6=0.10mm, respectively, with corresponding critical values ​​of 0.162mm, 0.122mm, 0.203mm, 0.149mm, 0.176mm, and 0.135mm. Displacement monitoring values ​​at each point are read within 10 minutes after blasting, and the calculated displacement increments do not exceed the critical values; therefore, the surrounding rock structure is determined to be stable.

[0043] Simultaneously integrating the hydraulic support working resistance data and FBG vibration spectrum data: the maximum working resistance of the hydraulic support within 30 minutes after blasting was 9800kN, lower than the rated value of 10800kN; the dominant frequency of the vibration spectrum was concentrated in 35–45Hz, with no significant low-frequency components. Based on this, the dynamic mine pressure manifestation intensity was calculated to be 0.82 (defined as the ratio of measured resistance to rated resistance multiplied by the low-frequency energy proportion correction factor). Since this is lower than the set safety threshold of 0.85, it was determined that the working face could safely pass through the blasting disturbance zone.

[0044] Proceed to step S005, continuously collect blasting response parameters at multiple preset time nodes: the immediate node (0–5 minutes) acquires the initial blasting response data; the mining impact node (when the working face advances to 15m from the blasting zone) reads the cumulative displacement increment, and the result is still below the critical value; no tension cracks or settlement troughs were detected by the surface SAR within 24 hours, so no surface deformation evolution data were generated; all data are linked in a time sequence to construct a response parameter set containing timestamps, spatial coordinates, physical quantity values, and judgment labels.

[0045] In step S006, actual blasting timing indicators are extracted from the response parameter set, and the blasting area is divided into zone A (0–10m), zone B (10–20m), and zone C (20–30m). The measured vibration amplitude changes in each zone are compared with the predicted values ​​from the blasting design software, and the relative deviation rate is calculated. Since all values ​​are less than the preset threshold of 5%, all zones are marked as verified zones, and no abnormal samples are generated.

[0046] Finally, in step S007, the verification results are fed back to the comprehensive evaluation generation stage. The target vibration amplitude change threshold is fine-tuned from 0.85 mm / s to 0.84 mm / s, the critical displacement threshold coefficient is adjusted from 1.35 to 1.33, and the mine pressure strength safety threshold remains unchanged at 0.85. The optimized blasting evaluation results are generated iteratively to guide the design of blasting parameters for the next cycle. The entire process, through multi-source heterogeneous data fusion, physical model-driven approach, and threshold criterion linkage, achieves a full-chain, quantifiable, and traceable intelligent evaluation of the micro-differential blasting effect, from the degree of fragmentation and surrounding rock stability to mining safety.

[0047] like Figure 2 As shown, a smart evaluation and analysis system for micro-delay blasting effects includes: Data acquisition module: used to collect multi-source monitoring data from micro-delay blasting sites, and perform time-series alignment and normalization preprocessing to construct a unified standard blasting dataset; Effect evaluation module: used to extract stress wave propagation physical characteristics based on the blasting dataset; use the stress wave propagation physical characteristics to perform multi-angle collaborative analysis on the blasting dataset, comprehensively judge the blasting fracturing effect, rock mass stability and mine pressure risk, and obtain evaluation results; The correlation module is used to collect various blasting response parameters at different time points based on the evaluation results, including initial blasting response, secondary fracturing, mining-induced damage, surface deformation, and changes in mine pressure. It performs chain correlation processing on various parameters in chronological order to construct a response parameter set containing temporal relationships. The zone monitoring module is used to extract actual blasting time sequence indicators from the response parameter set and combine them with spatial location information to divide the blasting area into multiple monitoring zones with different response characteristics. Verification and optimization module: This module is used to compare the actual blasting effect indicators of each monitoring zone with the predicted indicators in the evaluation results item by item to calculate the deviation rate and obtain the verification results of the blasting effect; the verification results are fed back to the evaluation result generation step to iteratively generate the optimized blasting evaluation results.

[0048] A computer-readable storage medium is characterized in that it is used to store computer-readable instructions, which, when read by a computer, enable the execution of the intelligent evaluation and analysis method for micro-delay blasting effects.

[0049] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for intelligent evaluation and analysis of micro-delay blasting effects, characterized in that, include: Multi-source monitoring data from micro-delay blasting sites were collected and preprocessed with time-series alignment and normalization to construct a unified standard blasting dataset. Extract the superposition intensity, energy coupling level, vibration attenuation law, propagation path, and physical characteristics of stress wave propagation by porous time-delay interference from the blasting dataset. By utilizing the physical characteristics of stress wave propagation, the blasting dataset is analyzed from multiple angles to comprehensively assess the blasting fracturing effect, rock mass stability, and mine pressure risk, thereby obtaining evaluation results. Based on the evaluation results, various blasting response parameters, including initial blasting response, secondary fracturing, mining-induced damage, surface deformation, and changes in mine pressure, were collected at different time points. These parameters were then chained together in chronological order to construct a response parameter set containing temporal relationships. The actual blasting time sequence indicators are extracted from the response parameter set and combined with spatial location information to divide the blasting area into multiple monitoring zones with different response characteristics. The deviation rate is calculated by comparing the actual blasting effect indicators of each monitoring zone with the predicted indicators in the evaluation results to obtain the verification results of the blasting effect. The verification results are fed back to the evaluation result generation step to iteratively generate optimized blasting evaluation results.

2. The intelligent evaluation and analysis method for micro-delay blasting effect according to claim 1, characterized in that, Collect multi-source monitoring data from the micro-delay blasting site, including: Multiple vibration sensors are deployed around the blasting area to record the vibration reference value and location coordinates of each sensor before the blast. A spatial relationship matrix is ​​constructed based on the positional relationship between the sensors and the blasting point. The influence range of the stress wave is calculated based on the spatial relationship matrix. Holes are drilled in the rock mass surrounding the influence range and displacement monitoring equipment is installed. The displacement reference value of each monitoring point before blasting is recorded to obtain displacement reference data. The spatial relationship matrix is ​​combined with the displacement reference data to determine the key blasting monitoring area. The surface deformation of the key blasting monitoring area is monitored using surface remote sensing equipment to obtain surface deformation characteristic data. The working resistance value and column pressure value of each hydraulic support are collected in real time on the hydraulic support of the working face corresponding to the blasting area, and the stress change of the hydraulic support before and after the blasting is recorded to obtain the stress data of the hydraulic support. High-sensitivity vibration frequency monitoring instruments are deployed at key locations in mine roadways to continuously monitor the vibration frequency of rock mass caused by blasting, record the spectral distribution characteristics and main frequency variation patterns of the vibration frequency, and obtain vibration frequency data within the mine. The vibration reference value, the displacement reference value, the surface deformation characteristic data, the hydraulic support force data, and the mine vibration frequency data are integrated to form a blasting dataset.

3. The intelligent evaluation and analysis method for micro-delay blasting effect according to claim 1, characterized in that, Based on the aforementioned physical characteristics of stress wave propagation, a multimodal collaborative evaluation is performed on the blasting dataset to obtain evaluation results, including: The physical characteristics of stress wave propagation specifically include the intensity of the superposition of stress waves generated by multiple blasting holes, the level of coupling between blasting energy and rock mass, the law of vibration intensity attenuation with distance, the propagation path of stress waves in rock mass, and the waveform interference characteristics caused by the delay of different blasting holes. A calculation model for the change in vibration amplitude is established based on the physical characteristics of stress wave propagation. The average value of vibration data measured at each monitoring point after blasting is subtracted from the average value of the vibration reference value before blasting to obtain the actual change in vibration amplitude. In the stress wave superposition region, any vibration sensor is selected as the reference monitoring point, and the target vibration amplitude change threshold to be achieved for the reference monitoring point is set according to the energy coupling requirements of micro-differential blasting. The actual vibration amplitude change at the benchmark monitoring point is compared with the target vibration amplitude change threshold to obtain the analysis results of the blasting and breaking effect. Specifically, when the actual vibration amplitude change at the benchmark monitoring point reaches or exceeds the target vibration amplitude change threshold, it indicates that the stress wave superposition is sufficient and the energy coupling is good, and the blasting effect is judged to have reached the complete fragmentation standard; when the actual vibration amplitude change at the benchmark monitoring point is lower than the target vibration amplitude change threshold, it indicates that the stress wave superposition is insufficient, and the blasting effect is judged to have only reached the local fragmentation standard. Based on the analysis results, a critical displacement threshold for rock mass structure instability is set. When the rock mass is determined to be completely broken, the critical displacement threshold is set to a first preset multiple of the pre-blasting displacement reference value to identify abnormal deformation. When the rock mass is determined to be partially broken, the critical displacement threshold is set to a second preset multiple of the pre-blasting displacement reference value to accommodate subsequent fracture. Based on the critical displacement threshold for rock mass instability, the evaluation results of rock mass structure stability are obtained; Based on the fusion analysis of hydraulic support working resistance data and vibration spectrum data, the dynamic mine pressure manifestation intensity when the working face passes through the blasting zone is analyzed to obtain the evaluation result of mine pressure manifestation risk. The analysis results, assessment results, and evaluation results are integrated to form a comprehensive evaluation result that includes three dimensions: crushing effect, structural stability, and mine pressure risk.

4. The intelligent evaluation and analysis method for micro-delay blasting effect according to claim 3, characterized in that, The rock mass structure stability assessment results include: when the analysis results determine that the local fracture standard is met, the displacement monitoring values ​​of each monitoring point after blasting are read, and the initial displacement benchmark value before blasting is subtracted from the displacement value after blasting to calculate the displacement increment of each monitoring point. The displacement increment of each monitoring point is compared with the critical displacement threshold for rock mass instability. When the displacement increment of any monitoring point exceeds the critical threshold, it is determined that the rock mass in the current monitoring area has collapsed, that is, the assessment result is unstable. When the displacement increment of all monitoring points does not exceed the critical threshold, it is determined that the rock mass structure is stable, that is, the rock mass structure stability assessment result is stable. The evaluation results include: setting a safety threshold for mine pressure intensity based on the safety requirement of fully releasing roof energy; comparing the mine pressure intensity obtained from comprehensive analysis with the safety threshold for mine pressure intensity; when the mine pressure intensity is lower than or equal to the safety threshold, it is determined that the working face can safely pass through the blasting area; when the mine pressure intensity exceeds the safety threshold, it is determined that there is a strong mine pressure risk, and the working face cannot pass directly and pressure relief measures must be taken.

5. The intelligent evaluation and analysis method for micro-delay blasting effect according to claim 1, characterized in that, Based on the evaluation results, various data are acquired and linked in a time-series manner to construct a set of response parameters, including: Based on the comprehensive evaluation results, multiple time nodes are set for continuous monitoring, including an immediate monitoring node when blasting is completed, a delayed monitoring node after a certain period of time after blasting, and a monitoring node for the impact of mining when the working face advances to a preset distance threshold. When the blasting is completed, the dynamic response signals output by all vibration sensors are collected at the first real-time monitoring node. The peak value of the dynamic response signal is subtracted from the initial vibration signal reference value before the blast to calculate the actual vibration amplitude change corresponding to each sensor, thus forming the initial blast response data. When the comprehensive evaluation result is determined to be a local fracture standard, it means that the rock mass will undergo secondary fracture. Then, the vibration response signal of the benchmark monitoring point is collected again at the delayed monitoring node after a preset time interval. If the actual vibration amplitude change reaches or exceeds the target vibration amplitude change threshold at this time, it is determined that secondary fracture has occurred, and secondary fracture monitoring data is recorded. When the comprehensive evaluation result determines that the rock mass structure remains stable, it is determined whether the mining impact during the working face advance has triggered premature collapse. When the mining impact monitoring node at the preset distance of the working face advances, the current displacement value of each monitoring point is read, and the cumulative displacement increment from blasting to the current moment is calculated. If the cumulative displacement increment exceeds the critical displacement threshold for rock mass structure instability, it is determined that premature collapse has been triggered, and the premature collapse data is recorded. When the comprehensive evaluation results determine that the working face cannot be directly passed, the mine pressure release process is monitored. The working face advance is suspended and the changes in mine pressure intensity are continuously monitored until the mine pressure intensity is reduced to below the mine pressure intensity safety threshold, and the dynamic changes in mine pressure are recorded. When tension cracks extending along the working face advance direction or settlement troughs are detected on the ground surface, it is determined that premature damage has occurred on the ground surface, and the data on the evolution of ground surface deformation are recorded. The initial blasting response data, the secondary fracturing monitoring data, the mining-induced damage data, the surface deformation evolution data, and the dynamic change data of mine pressure are linked and associated according to their respective time sequence to establish the temporal relationship between the data and construct a response parameter set.

6. The intelligent evaluation and analysis method for micro-delay blasting effect according to claim 5, characterized in that, The temporal chain association includes: A three-layer anomaly detection architecture is constructed using the multiple time-series monitoring nodes to perform hierarchical anomaly detection during the blasting execution process and subsequent mining impact stages. The first layer of detection uses a physical rule engine to match real-time monitoring data with multiple preset blasting anomaly rules, screen out dynamic behaviors that do not conform to the normal blasting response pattern, and identify abnormal blasting events. The second layer of detection performs correlation analysis on the abnormal blasting events and calculates risk scores. When there are two or more abnormal blasting events, the risk scores are graded and assessed according to multiple risk level assessment threshold ranges, and the corresponding level of early warning mechanism is triggered in sequence to form a graded early warning from low to high. The third layer of detection dynamically adjusts the warning threshold range and monitoring time interval based on the feedback from the graded warnings, so as to accurately define the risk level boundaries; The abnormal blasting event is associated with the initial blasting response data, the secondary fracturing monitoring data, the mining-induced damage data, the surface deformation evolution data, and the dynamic change data of the mine pressure in chronological order of occurrence. Each data point is then timestamped and stored in the blasting process response database. An intelligent diagnostic mechanism is constructed based on the response database. The intelligent diagnostic mechanism is driven by the physical mechanism of stress wave propagation. When an abnormal blasting event is detected, a traceability query program is triggered to locate the dynamic cause of the abnormality by analyzing the data. Based on the historical data stored in the response database and the analytical experience accumulated by the intelligent diagnostic mechanism, a knowledge graph of blasting effect evolution is established. The knowledge graph can identify time-series correlation patterns including the propagation path of stress waves during the initial blast, the spatial distribution of the insufficiently broken area, the triggering conditions of secondary fracturing, the advance range of mining impact, and the evolution law of mine pressure manifestation. The evolutionary knowledge graph quantifies the dynamic response characteristics of blasting effects throughout the entire process, from initial detonation, rock mass response, secondary fracturing, mining impact, to safe passage of the working face, forming a temporal chain-like correlation system of mutual influence among each stage.

7. The intelligent evaluation and analysis method for micro-delay blasting effect according to claim 1, characterized in that, The blasting area is intelligently spatially partitioned based on the set of response parameters, forming multiple monitoring zones, including: Three core indicators—stress wave superposition intensity, rock mass deformation response characteristics, and mining impact range—are extracted from the response parameter set. Combined with the spatial location information of sensor distribution location, spatial gradient change, deformation distribution range, surface impact area, and support stress distribution, the blasting area is divided into multiple monitoring zones with different response characteristics. The multiple monitoring zones are dynamically tracked. By analyzing the historical blasting data of each monitoring zone, evaluation units that exhibit stress wave superposition trends, frequent secondary fracture trends, stable rock mass structure trends, high incidence of mining-induced collapse trends, and abnormal mine pressure manifestation trends are identified. The development trend characteristics of the evaluation unit are correlated with the physical characteristics of stress wave propagation to obtain the mutual influence relationship between different monitoring zones and establish a response mechanism describing the coupling effect of blasting effect and mining behavior. Based on the aforementioned response mechanism, a spatial correlation matrix is ​​constructed. This spatial correlation matrix quantitatively characterizes the propagation path of stress waves in space, the attenuation law of energy with time and space, the spatial evolution characteristics of rock mass deformation, the spatial distribution law of secondary fracturing, and the spatial range of the impact of mining advance. Based on the mutual influence relationships in the spatial correlation matrix, a cross-regional dynamic response linkage mechanism is established. Through this linkage mechanism, the state change of any evaluation unit can automatically trigger the coordinated response of its associated evaluation units, thereby achieving coordinated monitoring of the main blasting area and the auxiliary monitoring area.

8. The intelligent evaluation and analysis method for micro-delay blasting effect according to claim 7, characterized in that, The actual blasting time sequence indicators of each monitoring zone are collected and compared with the blasting effect indicators in the evaluation results to obtain the verification results of the blasting effect, including: Collect actual blasting timing indicators for each monitoring zone; The blasting timing indicators include quantitative indicators such as the actual vibration amplitude change, the intensity of the stress wave superposition effect, the trigger time of secondary fracturing, the rock mass displacement increment, the surface deformation characteristics, the intensity of the mining pressure manifestation, the safe passage time of the working face, and the scope of the mining advance influence. The actual blasting timing index is compared with the blasting effect index in the evaluation results item by item, and the relative deviation rate is calculated. The formula for calculating the relative deviation rate is: Relative deviation rate = |actual value - predicted value| / actual value × 100%; A preset comparison threshold is set. When the relative deviation rate is less than or equal to the preset comparison threshold, the evaluation result is determined to be accurate and reliable, and the monitoring partition is marked as a verification-passed partition. When the relative deviation rate is greater than the preset comparison threshold, it is determined that the evaluation result has a deviation, the monitoring zone is marked as a deviation zone, and the geological conditions, blasting parameters and monitoring data of the deviation zone are extracted as abnormal samples. The number of verified areas in each monitoring zone is counted, and the abnormal samples are summarized to form a deviation dataset, resulting in a blasting effect verification result that includes verified zones, deviation zones, and deviation datasets.

9. A smart evaluation and analysis system for micro-delay blasting effects, used to execute a smart evaluation and analysis method for micro-delay blasting effects as described in any one of claims 1-8, characterized in that, include: Data acquisition module: used to collect multi-source monitoring data from micro-delay blasting sites, and perform time-series alignment and normalization preprocessing to construct a unified standard blasting dataset; Effect evaluation module: used to extract the physical characteristics of stress wave propagation based on the blasting dataset; By utilizing the physical characteristics of stress wave propagation, the blasting dataset is analyzed from multiple angles to comprehensively assess the blasting fracturing effect, rock mass stability, and mine pressure risk, thereby obtaining evaluation results. The correlation module is used to collect various blasting response parameters at different time points based on the evaluation results, including initial blasting response, secondary fracturing, mining-induced damage, surface deformation, and changes in mine pressure. It performs chain correlation processing on various parameters in chronological order to construct a response parameter set containing temporal relationships. The zone monitoring module is used to extract actual blasting time sequence indicators from the response parameter set and combine them with spatial location information to divide the blasting area into multiple monitoring zones with different response characteristics. Verification and optimization module: This module is used to compare the actual blasting effect indicators of each monitoring zone with the predicted indicators in the evaluation results item by item to calculate the deviation rate and obtain the verification results of the blasting effect; the verification results are fed back to the evaluation result generation step to iteratively generate the optimized blasting evaluation results.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable instructions, which, when read by a computer, enable the execution of a micro-delay blasting effect intelligent evaluation and analysis method as described in any one of claims 1-8.

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