A multi-dimensional sensor adaptive layout method in explosive cutting experiment

Abstract

The application provides a multi-dimensional sensor adaptive layout method in explosive cutting experiments, and relates to the technical field of explosive test testing, and comprises the following steps: synchronously collecting multiple types of experimental data to establish a parameter account book, three-dimensionally meshing an experimental site to form independent space units, matching sensor types according to regions, optimizing layout points based on quantitative rules, normalizing monitoring and correcting sensor positions and parameters, checking and archiving collected data in layers to form a closed-loop archive, and realizing subsequent experimental layout adaptive iteration by using historical archives. Through the means of establishing a full-link table linkage system, selecting and matching sensors according to space units, quantitatively optimizing layout points, online abnormal correction, grading data screening and archiving, and historical data prior reuse, the application realizes sensor adaptive layout, improves layout precision and collected data quality, relies on parameter variation to complete layout rapid iteration, and reduces repeated layout work.

Description

Technical Field

[0001] This invention relates to the field of explosion testing technology, and more specifically, to a multi-dimensional sensor adaptive layout method for explosion cutting experiments. Background Technology

[0002] As the application of explosive cutting technology in demolition and military testing continues to deepen, the demand for precision in explosive cutting experiments and tests is constantly increasing. Experiments need to rely on multi-dimensional sensors to simultaneously collect multiple physical parameters such as shock waves, fragments, and vibrations. The specifications of explosive charges, target configurations, and test site environments vary significantly. The control standards for sensor placement, model selection, subsequent data traceability, and scheme reuse are gradually increasing. The traditional extensive sensor placement mode is no longer suitable for the data acquisition requirements of high-precision explosive tests.

[0003] Traditional explosive cutting experiments rely heavily on operators' past experience to select locations and choose sensors, lacking standardized spatial division and quantitative optimization processes. This results in highly random and poorly adaptable placement. Furthermore, the absence of online sensor verification mechanisms means that location deviations and timing anomalies cannot be rectified in a timely manner. Experimental data is archived in a fragmented manner, without a complete data loop formed by interconnected forms. Historical experimental results cannot be transformed into prior experience to guide subsequent placement, leading to a large amount of repetitive placement work and high data acquisition errors.

[0004] Therefore, it is necessary to design a multi-dimensional adaptive sensor layout method for explosive cutting experiments to solve the problems of existing technologies that rely on manual experience to deploy sensors, making it difficult to adaptively optimize the location and preventing the closed-loop reuse and iteration of experimental data. Summary of the Invention

[0005] In view of this, the present invention proposes a multi-dimensional adaptive sensor layout method for explosive cutting experiments, which aims to solve the problems of existing technologies that rely on manual experience to deploy sensors, making it difficult to adaptively optimize the location and preventing experimental data from being reused and iterated in a closed loop.

[0006] In one aspect, this invention proposes a multi-dimensional sensor adaptive layout method for explosive cutting experiments, comprising: Simultaneously collect data related to the explosive cutting conditions, target, charge, and site space. Collect various types of data to form parameter sets. After integrating and summarizing the parameter sets, establish a parameter ledger and retain the original records. The experimental range is delineated by retrieving the site spatial parameters from the parameter ledger, the entire area is divided into multiple independent spatial units, the multiple independent spatial units are numbered, coordinates are measured and information is registered, a spatial unit registration form is generated and bound to the previous parameter ledger; By combining the space unit registration form and sensor installation requirements, a category matching list is compiled, and the sensors are bound to the corresponding space units one by one; by integrating all the data from the ledger, registration form and matching list, a point optimization calculation rule is constructed, the optimal deployment point is calculated and filtered to obtain the optimal deployment point, the sensor is installed on site with reference to the point information, and a point deployment record table is generated and associated with all front-end forms. Based on the point deployment record table, the sensors are connected to carry out routine status and signal monitoring, the offset data is extracted and compared with the original coordinates to complete the point correction, and various related forms are updated synchronously; all sensor signals, time information and ledger data are integrated, and the data is sorted and archived by category and time sequence, and the data closed-loop flow is realized by relying on the full-link related forms.

[0007] Furthermore, when dividing the entire domain into multiple independent spatial units, this includes: The defined experimental area is divided into three-dimensional grids, and each grid node is used as a candidate spatial unit. Based on the obstacle distribution, signal obstruction estimate, and safety distance requirements in the site spatial parameters, the candidate spatial units are initially filtered to eliminate units that do not meet the installation feasibility. The remaining units are determined as independent spatial units, and numbering and information registration are completed based on the three-dimensional coordinates.

[0008] Furthermore, when compiling a category matching list by combining the aforementioned space unit registration form and sensor installation requirements, the following is included: A first type of sensor matching priority, a second type of sensor matching priority, and a third type of sensor matching priority are preset, wherein the first type of sensor matching priority is higher than the second type of sensor matching priority, and the second type of sensor matching priority is higher than the third type of sensor matching priority; Based on the sensor's signal type, measurement range, and protection level, sensors are classified into key sensing, general monitoring, and auxiliary acquisition categories. When the space unit is located within the predicted shock wave overpressure peak area, the space unit is bound to a key sensing type sensor and the first type of sensor is assigned a matching priority. When the space unit is located outside the estimated shock wave overpressure peak area and within the coverage area of ​​the fragment scattering trajectory, the space unit is bound to a general monitoring sensor and a second type of sensor is assigned a matching priority. When a space unit is located outside the estimated shock wave overpressure peak area and outside the coverage area of ​​the fragment scattering trajectory, the space unit is bound to an auxiliary acquisition sensor and a third type of sensor is assigned a matching priority.

[0009] Furthermore, when constructing location optimization calculation rules by integrating all data from the aforementioned ledgers, registration forms, and matching lists, and calculating and filtering to obtain the optimal deployment locations, the following steps are included: Obtain the estimated sensor signal attenuation value, the distance between the spatial unit and the explosion cutting center, and the density of obstructions around the unit for each spatial unit with bound sensors; Spatial units whose sensor signal attenuation estimate is less than a preset attenuation threshold are marked as first preferred units, and spatial units whose sensor signal attenuation estimate is greater than or equal to the preset attenuation threshold are marked as second preferred units. Spatial units whose distance from the explosion cutting center is less than a preset safe distance threshold are marked as risk units; spatial units whose distance is greater than or equal to the preset safe distance threshold but less than the effective monitoring distance threshold are marked as effective monitoring units; and spatial units whose distance is greater than or equal to the effective monitoring distance threshold are marked as edge monitoring units. The first preferred unit further selects spatial units that are simultaneously marked as effective monitoring units as the optimal deployment points; When there is no spatial unit that simultaneously satisfies both the first preferred unit and the effective monitoring unit, the spatial units marked as effective monitoring units in the second preferred unit are selected as candidate deployment points. Based on the density values ​​of the surrounding obstructions, the spatial units with the lowest density values ​​are selected as the optimal deployment points.

[0010] Furthermore, when conducting routine status and signal monitoring of the sensors based on the aforementioned point layout record table, and extracting offset data to compare with the original coordinates to complete point correction, the process includes: Obtain the real-time signal strength, signal time synchronization deviation, and physical location detection coordinates of each installed sensor; Sensors whose real-time feedback signal strength is lower than a preset signal strength threshold are marked as signal abnormal sensors; sensors whose signal time synchronization deviation is greater than a preset time deviation threshold are marked as timing abnormal sensors; and sensors whose physical position detection coordinates deviate from the original coordinates by a distance greater than a preset displacement deviation threshold are marked as displacement abnormal sensors. When any sensor is simultaneously marked as a signal abnormality sensor and a displacement abnormality sensor, it is determined that the sensor needs to perform physical position correction. A displacement correction vector is generated based on the difference between the physical position detection coordinates and the original coordinates. The installation coordinates of the sensor are re-determined based on the displacement correction vector, and the corresponding coordinate information in the point layout record table is updated. When any sensor is marked as a timing-abnormal sensor, it is determined that the sensor needs to perform parameter calibration, the local time base of the sensor is adjusted to synchronize with the experimental master clock, and the calibration timestamp is recorded in the associated form.

[0011] Furthermore, when integrating all sensor signals, time information, and ledger data, and completing data organization and archiving by category and time sequence, and achieving closed-loop data flow based on end-to-end related forms, this includes: Acquire the shock wave arrival time data, pressure peak data, and vibration spectrum data collected by all sensors during the explosive cutting experiment, and obtain the spatial unit number and timestamp information corresponding to the above data; The shock wave arrival time data are categorized by spatial unit number to form the first data subset, the pressure peak data are categorized by spatial unit number to form the second data subset, and the vibration spectrum data are categorized by spatial unit number to form the third data subset. Extract the arrival time difference of each spatial unit in the first data subset, and mark and remove abnormal data whose arrival time difference exceeds the preset time window range; Extract the relative deviation between the pressure peak value and the theoretically predicted peak value of each spatial unit in the second data subset. When the relative deviation exceeds the preset deviation threshold, perform a reverse association query on the sensor category matching priority and point layout record corresponding to the spatial unit, generate a deviation analysis report and attach it to the archived data. The first, second, and third data subsets, after removing outliers, are merged in timestamp order to form a complete time-series data chain. This complete time-series data chain is then linked with multiple tables, including the parameter ledger, spatial unit registration form, category matching list, and site layout record form, to generate a closed-loop archive of experimental data.

[0012] Furthermore, when extracting the arrival time difference of each spatial unit in the first data subset, and marking and removing abnormal data whose arrival time difference exceeds the preset time window range, the process includes: A first time window threshold, a second time window threshold, and a third time window threshold are preset, wherein the first time window threshold is less than the second time window threshold, and the second time window threshold is less than the third time window threshold. The theoretical shock wave arrival time window corresponding to the spatial unit is determined based on the distance between the spatial unit and the explosion cutting center. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is less than or equal to the first time window threshold, the data is marked as high-confidence data and retained. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is greater than the first time window threshold and less than or equal to the second time window threshold, the data is marked as acceptable data and retained, and a time deviation mark is attached when archiving. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is greater than the second time window threshold and less than or equal to the third time window threshold, the data is marked as questionable data. The system then checks whether the sensor corresponding to the space unit has undergone point calibration or parameter calibration. If calibration has been performed, the data is downgraded to acceptable data. If calibration has not been performed, the data is marked as data to be reviewed. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is greater than the threshold of the third time window, the data is marked as abnormal data and removed, and an abnormal event record is generated and associated with the spatial unit.

[0013] Furthermore, when extracting the relative deviation between the peak pressure of each spatial unit in the second data subset and the theoretically estimated peak pressure, and when the relative deviation exceeds a preset deviation threshold, a reverse correlation query is performed on the sensor category matching priority and location deployment records corresponding to that spatial unit. This generates a deviation analysis report, which is then appended to the archived data. This includes: Obtain the sensor category matching priority corresponding to the spatial unit, and obtain the calibration history marker of the spatial unit in the point deployment record table; When the relative deviation exceeds the preset deviation threshold and the sensor category matching priority is the first type of sensor matching priority, it is determined to be a key data deviation, and a first-level deviation analysis report is generated. The report includes the relative deviation value, the theoretical estimated peak value, the actual pressure peak value, and the detailed three-dimensional coordinates of the spatial unit. When the relative deviation exceeds the preset deviation threshold and the sensor category matching priority is the second type of sensor matching priority, it is determined to be a general data deviation. Further check whether the spatial unit has a calibration history mark. If it has a calibration history mark, generate a secondary deviation analysis report and suggest recalibrating the sensor. If it does not have a calibration history mark, generate a secondary deviation analysis report and suggest checking the sensor installation status. When the relative deviation exceeds the preset deviation threshold and the sensor category matching priority is the third type of sensor matching priority, it is determined to be auxiliary data deviation. Only a level 3 deviation analysis report is generated as reference information, and no verification suggestion is triggered.

[0014] Furthermore, after generating the closed-loop archive of experimental data, it also includes: The closed-loop archive of experimental data is linked to the parameter log of the next explosive cutting experiment. The optimal deployment points, sensor category matching records, and point calibration history in the experiment are used as prior knowledge and input into the point optimization calculation rules of the next experiment. When the similarity between the site spatial parameters of the next experiment and the site spatial parameters of the experiment is greater than the preset similarity threshold, the spatial unit number of the optimal layout point in the experiment is directly reused, skipping some of the screening steps in the point optimization calculation. When the similarity is less than or equal to the preset similarity threshold, the deviation analysis report in the experiment is used as a weighted correction factor input to the point optimization operation rule to adjust the priority score of each spatial unit.

[0015] Furthermore, after linking the closed-loop archive of experimental data with the parameter log for the next explosive cutting experiment, it also includes: Obtain the pre-calibration parameters for the next explosive cutting experiment. The pre-calibration parameters include the charge amount adjustment value, the target arrangement offset, and the site space fine-tuning data. The pre-calibrated parameters are compared item by item with the corresponding parameters in the closed-loop archive of historical experimental data, and the rate of change of each parameter is calculated. When the absolute value of the rate of change of the charge adjustment value is greater than the first preset rate of change threshold, it is determined that the shock wave coverage area has changed. Based on the changed coverage area, the estimated shock wave overpressure peak area and fragment scattering trajectory coverage area are re-determined, and the spatial unit and sensor binding relationship in the category matching list are updated simultaneously. When the absolute value of the rate of change of the target arrangement offset is greater than the second preset rate of change threshold, it is determined that the explosion cutting center has been displaced. The distance values ​​between all independent spatial units in the spatial unit registration table and the explosion cutting center are corrected in reverse according to the displacement vector. The corrected distance values ​​are then input into the point optimization operation rules to recalculate the division of effective monitoring units and edge monitoring units. When the absolute value of the rate of change of the site space fine-tuning data is greater than the third preset rate of change threshold, it is determined that the distribution of site obstacles or signal obstruction conditions have changed, and the preliminary filtering step of candidate space units is re-executed to remove newly added units that do not meet the installation feasibility. The updated category matching list, spatial unit registration form, and point optimization calculation rules are integrated to generate an incremental layout adjustment scheme. In this scheme, only the spatial units and sensor binding relationships that have changed are marked, while the historical optimal placement points of the unchanged parts are retained, thus completing the adaptive and rapid iteration of the sensor layout.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: By using parameter ledgers, three-dimensional spatial unit division, and hierarchical matching optimization, the sensor adaptive placement is achieved, avoiding the randomness of manual placement. Online monitoring and calibration ensure the stability of sensor acquisition. Layered data verification and archiving construct a closed-loop data system. Historical experimental archives can also be used as prior knowledge to adaptively iterate subsequent placement schemes based on changes in parameters such as charge, target, and site. This effectively improves the accuracy of sensor placement and the reliability of experimental data, while reducing the workload of repetitive placement. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1A flowchart illustrating the adaptive layout method for multi-dimensional sensors in an explosive cutting experiment provided in this embodiment of the invention. Detailed Implementation

[0018] Exemplary embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the invention to those skilled in the art. It should be noted that, without conflict, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] It is important to note that: The parameter set refers to the data set formed by summarizing four types of related collected data: explosive cutting conditions, target, charge, and site space. It is used to integrate various types of original measured data and provide a data source for establishing a parameter ledger.

[0020] The parameter ledger refers to the summary ledger data established by integrating all parameter sets. It is used to bind various forms such as the spatial unit registration form and to support the entire process of spatial division, sensor selection and optimization.

[0021] Original records refer to the initial original data vouchers retained during the process of collecting various experimental data. They are used to retain the original measured information and ensure that the data can be traced and verified throughout the entire process.

[0022] Site spatial parameters refer to the spatially relevant measured data such as the distribution of site obstacles, signal obstruction, and safety distance required to delineate the experimental area. These data are used to divide the entire experimental area and screen available candidate spatial units.

[0023] Independent spatial units refer to usable grid units that remain after three-dimensional meshing and elimination of infeasible installation points. They are used to bind corresponding types of sensors and serve as the basic point carriers for sensor deployment.

[0024] The spatial unit registration form is a registration form that records the number, three-dimensional coordinates, and unit attributes of independent spatial units. It is used to link parameter ledgers and to help compile a sensor category matching list.

[0025] The site optimization calculation rule refers to the quantitative screening and judgment criteria constructed by integrating data from ledgers, registration forms, and matching lists. It is used to calculate and select the optimal deployment site based on attenuation, distance, and occlusion parameters.

[0026] The optimal deployment point refers to the final installation location of the sensor obtained by selecting the best location according to the point optimization calculation rules. It is used to guide the on-site deployment of sensors and generate a point deployment record table.

[0027] Candidate spatial units refer to all the grid nodes generated after the experimental area is three-dimensionally meshed, and are used to select independent spatial units after filtering by obstacles, occlusions, and safety conditions.

[0028] Key sensor category refers to the highest priority category of dedicated sensors that are adapted to the overpressure peak area of ​​shock waves. They are used to be deployed in the peak area of ​​shock waves to accurately collect key overpressure test data.

[0029] General monitoring type refers to conventional sensor category that is adapted to the area covered by fragment scattering and has a medium priority. It is used to be deployed within the fragment trajectory range to complete the monitoring and data collection of conventional explosion conditions.

[0030] Auxiliary data acquisition refers to the lowest priority sensor category, which is deployed outside the range of shock waves and fragments. It is used to assist in the acquisition of data from the surrounding environment of the test area and to improve the overall data dimensions.

[0031] The first preferred unit refers to a spatial unit whose signal attenuation estimate is lower than a preset attenuation threshold. It is used to select points that meet the distance conditions as the optimal deployment points.

[0032] The second preferred unit refers to a spatial unit whose signal attenuation estimate is greater than or equal to a preset attenuation threshold. It is used to select some points as deployment options when there is no qualified first preferred unit.

[0033] Risk units refer to spatial units whose distance from the explosion center is less than a preset safe distance threshold. They are used to directly screen out and avoid the risk of sensor damage from the explosion.

[0034] An effective monitoring unit refers to a spatial unit whose distance is greater than the safety threshold but less than the effective monitoring distance threshold, and is used as the core candidate spatial range for screening optimal locations.

[0035] Edge monitoring units refer to spatial units whose distance from the explosion center exceeds the effective monitoring distance threshold. They are used to mark monitoring boundary points and are generally not prioritized for deployment.

[0036] A signal anomaly sensor is a sensor whose real-time signal strength is lower than a preset signal strength threshold. It is used to determine sensor signal faults and help distinguish the types of sensor anomalies.

[0037] A timing anomaly sensor is a sensor whose signal time synchronization deviation exceeds a preset time deviation threshold. It is used to determine clock asynchrony faults and serves as a basis for parameter correction.

[0038] The preset displacement deviation threshold refers to the permissible offset limit between the measured coordinates of the sensor and the original coordinates, which is used to identify whether the sensor has experienced abnormal physical displacement.

[0039] A displacement anomaly sensor is a sensor whose coordinate offset exceeds a preset displacement deviation threshold. It is used to determine sensor objects that require physical position correction.

[0040] The displacement correction vector is a position correction vector generated from the difference between the measured coordinates and the original installation coordinates. It is used to convert the new installation coordinates and complete the physical position correction of the sensor.

[0041] The first data subset refers to the dataset formed by aggregating all shock wave arrival time data according to spatial unit number, which is used to conduct arrival time verification and eliminate test data with time series anomalies.

[0042] The second data subset refers to the dataset formed by aggregating all pressure peak data according to spatial unit number. It is used to compare with theoretical peak values ​​and investigate deviations in pressure acquisition data.

[0043] The third data subset refers to the dataset formed by aggregating all vibration spectrum data according to spatial unit number. It is used to retain vibration test data and participate in splicing a complete time-series data chain.

[0044] A complete time-series data chain refers to a coherent dataset formed by merging three data subsets according to timestamps after removing outlier data. It is used to associate various forms and generate closed-loop archives of experimental data.

[0045] Highly reliable data refers to collected data whose actual arrival time and the difference between the theoretical window center time and the actual arrival time do not exceed the threshold of the first time window. This data is used for direct archiving and retention and is considered accurate and effective experimental data.

[0046] Acceptable data refers to collected data whose time difference is between the thresholds of the first and second time windows, and is used for normal archiving after being marked with deviation.

[0047] Questionable data refers to collected data whose time difference is between the thresholds of the second and third time windows. It is used to verify the sensor calibration history before determining the archiving and disposal method.

[0048] Abnormal data refers to collected data whose time difference exceeds the threshold of the third time window. It is used to directly remove and retain abnormal records, and is not included in the formal archived data.

[0049] Key data deviation refers to data anomalies where the measured peak pressure of a first-priority key sensing sensor exceeds the deviation threshold. This is used to trigger a first-level deviation analysis report, which focuses on verifying the causes of the test anomalies.

[0050] A Level 1 Deviation Analysis Report is a specialized analysis document corresponding to key data deviations. It is used to record information such as deviation values ​​and coordinates, and serves as archival material for key experimental anomalies.

[0051] General data deviation refers to the abnormal situation where the data of the second priority general monitoring sensor exceeds the deviation threshold. It is used to generate a secondary report and to check the sensor installation or calibration status as needed.

[0052] A Level 2 Deviation Analysis Report is an analysis document corresponding to general data deviations, used to provide calibration or installation verification recommendations based on the correction history.

[0053] Auxiliary data deviation refers to the abnormal situation where the data of the third priority auxiliary acquisition sensor exceeds the deviation threshold. It is used to generate only a filing report and does not initiate on-site equipment verification.

[0054] A Level 3 Deviation Analysis Report is a simplified reference document corresponding to the deviation of auxiliary data. It is used for archiving and future reference only and serves as a reference for experimental data.

[0055] The weighted correction factor refers to the weighting parameter derived from the historical test deviation analysis report. It is used to correct the priority score of each spatial unit within the optimization rule when the site similarity is low.

[0056] Operating condition related data refers to the experimental operating condition data during the explosive cutting operation, which is collected and compiled into a parameter set to build a parameter ledger; the operating condition information of the entire explosive cutting process is recorded through on-site measurement, and the information is summarized and organized to form operating condition related data.

[0057] Target-related data refers to the parameter data of the test component, such as the size, material, and placement of the target, which is used to improve the basic parameter record and help to delineate the explosion influence range. The target-related data is obtained by summarizing and organizing the measured physical specifications and placement parameters of the target.

[0058] Charge-related data refers to explosive parameters such as charge specifications, charge quantity, and charge placement location. These parameters are used to predict the shock wave and fragmentation dispersion area and to support sensor selection and matching. Charge-related data is obtained by statistically analyzing the charge design parameters and actual filling data and summarizing them.

[0059] Site space-related data refers to site parameter data such as the size of the test area, the arrangement of obstacles, and the spatial occlusion conditions. It is used to divide the test area and screen available independent spatial units. The site environmental parameters are tested through on-site surveys and then summarized and organized to form site space-related data.

[0060] The sensor installation requirements for compiling a category matching list refer to a sensor model binding form compiled based on the spatial unit attributes and sensor installation specifications, used to match spatial units with different types of sensors one by one; by combining the spatial unit registration form and various sensor installation standards to match and classify each item, the compiled table is the category matching list.

[0061] The point layout record form is a filing form that records the coordinates, unit number, and sensor model of each sensor installation point. It is used to guide sensor calibration, data traceability, and form linkage updates. The point layout record form is formed by registering and filing the on-site installation information of the optimal layout points item by item.

[0062] Offset data refers to the positional change data of the sensor relative to its original coordinates, which is used to determine whether the sensor has experienced abnormal displacement and to initiate position correction. The positional difference is obtained by comparing the real-time measured coordinates with the original installation coordinates.

[0063] The original coordinates refer to the standard installation coordinate values ​​registered when the sensor is initially deployed, which serve as the reference for point calibration. The original coordinates are obtained by calibrating the optimal deployment point on site, entering the coordinate information, and archiving the data.

[0064] Sensing signals refer to the raw acquisition signals such as pressure, vibration, and time sequence collected by various sensors in explosion tests. These signals are used to classify and summarize them to generate various subsets of test data. The physical signals collected by sensors in real time during the test are then summarized and exported as sensing signals.

[0065] Time information refers to the unified time-series marker data corresponding to the data collected by the sensors, which is used to splice a complete time-series data chain based on the timestamp; by binding the test master clock to synchronously record the generation time of each sensor signal, the recorded data is the time information.

[0066] Ledger data refers to a complete set of parameter ledgers compiled from various experimental parameters. It is used to support spatial division, site optimization, and data archiving throughout the entire process. The ledger data is created by integrating and archiving all parameter sets and compiling them into a book.

[0067] Obstacle distribution refers to the spatial arrangement information of various obstructing structures and test components within the test site, used to filter candidate spatial units where sensors cannot be deployed; obstacle distribution data is obtained by surveying the location information of obstacles on-site and compiling and archiving the data.

[0068] Signal obstruction prediction is a predicted value that indicates the attenuation effect of obstacles on sensor signals. It is used to assist in screening available deployment units and calculating signal attenuation parameters. By acquiring and analyzing historical obstruction data from past explosion test sites, the corresponding obstruction impact estimate is obtained, and this estimate is the signal obstruction prediction.

[0069] The safety distance requirement refers to the minimum deployment distance standard for sensors set to avoid damage from explosions. It is used to eliminate risky units and screen compliant deployment points. The critical safety distance is obtained by acquiring and analyzing historical data on safety defenses from past explosion tests. This critical distance is the safety distance requirement.

[0070] The first category of sensor matching priority refers to the highest selection priority level corresponding to key sensing sensors, which is used to prioritize the selection of key sensors in the peak area of ​​shock waves. By pre-setting three-level priority sorting rules and assigning levels, the ranking result is the first category of sensor matching priority.

[0071] The second type of sensor matching priority refers to the medium selection priority level corresponding to general monitoring sensors, which is used to select general sensors in areas covered by fragments. By pre-setting the three-level priority sorting rules and assigning levels, the ranking result is the second type of sensor matching priority.

[0072] The third type of sensor matching priority refers to the lowest selection priority level corresponding to auxiliary acquisition sensors, which is used to select auxiliary sensors outside the test area. By pre-setting the three-level priority sorting rules and assigning levels, the ranking result is the third type of sensor matching priority.

[0073] Signal type refers to the types of physical signals that a sensor can collect, such as pressure, vibration, and timing signals. It is used to classify sensor types and achieve graded matching. The types of signals collected are determined by consulting the factory parameters of each sensor, and the summaries and records are the signal types.

[0074] Measurement range refers to the range of physical parameters that a sensor can normally collect data from. It is used to select suitable sensor models in conjunction with test conditions. The parameter obtained by referring to the range marked in the sensor's factory technical specifications is the measurement range.

[0075] Protection rating refers to the sensor's protection performance parameters against impact and fragmentation damage. It is used to screen sensors with appropriate protection specifications for areas with explosion hazards. The protection rating is obtained by checking the sensor's factory protection specifications and recording the parameters.

[0076] The estimated shock wave overpressure peak area refers to the high-pressure concentrated space range of the shock wave formed by predicting the charge parameters, which is used to bind key sensing sensors. The high-pressure space boundary range is obtained by acquiring and analyzing historical data of charge and shock wave distribution, and this range is the estimated shock wave overpressure peak area.

[0077] The fragment scattering trajectory coverage range refers to the spatial range of fragment scattering predicted based on the parameters of the charge and target, and is used to deploy general monitoring sensors; the scattering boundary range is obtained by acquiring and analyzing historical data of fragment scattering from past explosions, and this range is the fragment scattering trajectory coverage range.

[0078] The sensor signal attenuation estimate is a predicted value for sensor signal loss caused by site obstruction, used to divide the first and second preferred units. The attenuation estimate is obtained by acquiring and analyzing historical data on site obstruction and signal loss, and this value is the sensor signal attenuation estimate.

[0079] The distance between a spatial unit and the explosion cutting center refers to the spatial distance between the coordinates of each independent spatial unit and the explosion initiation center point. It is used to delineate risk, effective monitoring, and edge monitoring units. The distance between the spatial unit and the explosion center is calculated by measuring the distance between the three-dimensional coordinates of the spatial unit and the coordinates of the explosion center.

[0080] The density value of obstructions around a unit refers to the quantitative value of the density of obstacles around a spatial unit. It is used to select the best placement points when there are no preferred units. The quantitative value is calculated by statistically analyzing the volume of obstructions around the unit on-site. The result is the density value of obstructions around the unit.

[0081] The preset attenuation threshold is a pre-set critical value for distinguishing the quality of signals, used to divide the first preferred unit and the second preferred unit. The critical limit is obtained by acquiring and analyzing historical data of sensor signal attenuation, and this limit is the preset attenuation threshold.

[0082] The preset safety distance threshold refers to the pre-defined critical distance between sensor deployments used to identify risk units. The critical distance is obtained by acquiring and analyzing historical data on past explosions and equipment damage, and this critical distance is the preset safety distance threshold.

[0083] The effective monitoring distance threshold refers to the maximum distance limit for effective data collection by the sensor, which is set in advance and is used to distinguish between effective monitoring units and edge monitoring units. The distance threshold is obtained by acquiring and analyzing historical data of effective sensor collection distance.

[0084] The preset quantity refers to the number of optimal locations to be selected in advance during the screening of candidate locations. It is used to select and deploy locations in the second optimization unit. The number of selections is set in advance according to the total number of test deployments. The set value is the preset quantity.

[0085] Real-time signal strength refers to the signal amplitude parameter uploaded in real time during the operation of the sensor, which is used to determine whether the sensor has signal abnormalities; the collected data is the real-time signal strength, which is obtained by the sensor collecting the amplitude of the transmitted signal online in real time.

[0086] The signal time synchronization deviation value refers to the timing difference between the sensor's local clock and the test master clock, which is used to identify sensors with timing abnormalities. The difference is obtained by comparing the sensor's local time with the master clock reference time.

[0087] Physical position detection coordinates refer to the actual installation coordinates of the sensor obtained from the on-site inspection. They are used to compare with the original coordinates to determine displacement anomalies. The sensor points are measured using on-site coordinate instruments, and the measured data is the physical position detection coordinates.

[0088] The preset signal strength threshold is a pre-set lower limit of the acceptable signal amplitude, which serves as a criterion for judging signal abnormalities. The amplitude threshold is obtained by acquiring and analyzing historical data of normal sensor signals.

[0089] The preset time deviation threshold is a pre-set maximum allowable deviation limit for clock synchronization, used to identify timing abnormality sensors. The deviation threshold is obtained by acquiring and analyzing historical clock synchronization error data.

[0090] The difference between the original coordinates refers to the difference between the measured coordinates and the initial calibration coordinates, which is used to generate the displacement correction vector. The difference between the original coordinates is calculated by subtracting the corresponding original coordinates from the physical position detection coordinates.

[0091] The master clock is the unified reference timing clock source for the entire test system, used to unify the time reference of all sensors. The standard timing sequence is output through the built-in reference timing device of the test control system, and this reference clock is the master clock.

[0092] Shock wave arrival time data refers to the data collected at the moment the shock wave arrives at each deployment point, which is used to form the first data subset for timing verification; by capturing the time information of the moment the shock wave arrives through each sensor, the data is summarized as shock wave arrival time data.

[0093] Peak pressure data refers to the maximum overpressure value of the shock wave collected by the sensor, which is used to construct a second data subset for peak deviation comparison; the peak pressure at the instant of the explosion is measured by the pressure sensor, and the collected data is the peak pressure data.

[0094] Vibration spectrum data refers to the frequency domain acquisition parameters of site vibrations caused by an explosion, which are used to build a third data subset to improve the time series archive; vibration frequency domain information is collected throughout the process by vibration sensors, and the collected data is vibration spectrum data.

[0095] The spatial unit number refers to the unique code assigned to each independent spatial unit when it completes registration. It is used to classify and collect various types of data by location. Each spatial unit is assigned a unique code through the spatial unit registration process, and the assigned code is the spatial unit number.

[0096] Timestamp information refers to the baseline time sequence marker that binds each piece of collected data, which is used to assemble a complete time-series data chain in chronological order; the generation time of each piece of data is synchronously marked by the experimental master clock, and the marking information is the timestamp information.

[0097] The arrival time difference refers to the difference between the measured arrival time of the shock wave and the theoretical arrival time. It is used to eliminate abnormal time series data based on the time window. The difference is calculated by subtracting the theoretical arrival time from the measured arrival time.

[0098] The preset time window range refers to the pre-defined allowable fluctuation range of the measured arrival time, which is used to classify and determine high-confidence, doubtful, and abnormal data. The critical value of the range is obtained by acquiring and analyzing historical data of shock wave arrival time fluctuations, and this range is the preset time window range.

[0099] Peak pressure refers to the maximum pressure value of a single-point explosion obtained by the sensor, which is used to compare with the theoretically predicted peak pressure to calculate the relative deviation; the peak pressure of the explosion is collected on-site by the pressure sensor, and the measured value is the peak pressure.

[0100] The theoretical peak value refers to the maximum theoretical pressure calculated based on the parameters of the charge and the site. It is used to compare the measured peak value with the deviation of the data. The theoretical pressure value is calculated by acquiring and analyzing historical data of charge operation conditions. The calculation result is the theoretical peak value.

[0101] Relative deviation refers to the percentage of the measured peak pressure relative to the theoretically predicted peak pressure. It is used to determine whether the data exceeds the deviation threshold. The relative deviation is calculated by comparing the measured peak pressure with the theoretical peak pressure.

[0102] The preset deviation threshold is a pre-set maximum allowable deviation ratio limit for pressure data, which serves as a standard for judging data anomalies. The critical percentage of deviation is obtained by acquiring and analyzing historical pressure acquisition deviation data, and this critical percentage is the preset deviation threshold.

[0103] The first time window threshold refers to the first-level time tolerance limit for classifying time series data, used to delineate the boundary of high-confidence data. The first-level critical difference is obtained by acquiring and analyzing historical data of qualified time series data, and this critical difference is the first time window threshold.

[0104] The second time window threshold refers to the second-level time tolerance limit for classifying time series data, used to delineate the boundary between acceptable and questionable data. The second-level critical difference is obtained by acquiring and analyzing historical data of past time series error classification, and this critical difference is the second time window threshold.

[0105] The third time window threshold refers to the third-level time tolerance limit for classifying time series data, used to delineate the boundary between questionable and abnormal data. The third-level critical difference is obtained by acquiring and analyzing historical invalid time series data, and this critical difference is the third time window threshold.

[0106] The theoretical shock wave arrival time window refers to the range of theoretical arrival time of the shock wave calculated based on the distance between the unit cells, which serves as a benchmark for comparison with the measured time. The theoretical time interval is calculated by measuring the distance between the spatial unit cells and the explosion center, and the calculated interval is the theoretical shock wave arrival time window.

[0107] The center value of the time window refers to the midpoint reference time of the theoretical arrival time interval of the shock wave, which serves as the benchmark for calculating the measured time difference. The center value of the time window is calculated by obtaining the value at the midpoint of the theoretical arrival time window interval.

[0108] The absolute value of the difference refers to the positive value of the difference between the measured arrival time and the center value of the time window, which is used to classify and identify data by comparing the three-level time window threshold. The absolute value of the difference is calculated by taking the non-negative value of the difference between the measured time and the center value.

[0109] Sensor category matching priority refers to the first, second, and third grade selection levels of sensors bound to each space unit, which are used to generate different levels of analysis reports when data exceeds the tolerance; the corresponding priority is pre-defined by the explosion impact area where the space unit is located, and the defined level is the sensor category matching priority.

[0110] The calibration history mark refers to the remarks mark recorded in the point deployment record table for past calibration operations of the sensor. It is used to check the historical rectification status of the equipment when there is a general data deviation. The remarks mark is added to the form after each calibration of the sensor.

[0111] The relative deviation value refers to the specific percentage of deviation between the measured pressure and the theoretical peak value, and is included in the first-level deviation analysis report for archiving. The relative deviation value is obtained by calculating the percentage of deviation between the measured peak value and the theoretical peak value.

[0112] The detailed three-dimensional coordinates of a spatial unit refer to the three-dimensional spatial positioning parameters of the corresponding abnormal point, which are used to enter the first-level deviation analysis report to achieve point tracing. By retrieving the three-dimensional positioning parameters registered and retained in the early stage of spatial unit registration, the detailed three-dimensional coordinates of the spatial unit can be obtained.

[0113] The experimental data closed-loop archive refers to a complete set of experimental summary archives after multiple forms are linked, data is validated and archived, and it is used as prior data to guide the optimization of subsequent experimental layout. By linking multiple subset data with various ledger forms and integrating and archiving them, the integrated document is the experimental data closed-loop archive.

[0114] Similarity refers to the quantitative proportion of overlap between the spatial parameters of two experimental sites, used to determine whether the historical optimal layout points can be reused; the overlap ratio is calculated by comparing the parameters of the old and new sites item by item, and the result is the similarity.

[0115] The preset similarity threshold refers to the pre-set critical overlap ratio of site parameters, which serves as the criterion for determining the reuse of site plans. The critical overlap ratio is obtained by acquiring and analyzing historical data of site reuse from multiple batches, and this critical ratio is the preset similarity threshold.

[0116] Pre-calibration parameters refer to the adjustment parameters of the charge, target, and site determined in advance before the next round of experiments, which are used to predict whether the layout needs to be adaptively adjusted; by calibrating various adjustment parameters in the early stage of the new round of experiments, the parameters are summarized as pre-calibration parameters.

[0117] The charge adjustment value refers to the change in charge value between the current test and the historical test, and is used to determine whether the coverage area of ​​the shock wave has changed. The charge adjustment value is calculated by making a difference between the charge parameters of the new and old tests.

[0118] The target placement offset refers to the displacement value of the target in this round relative to its historical placement position, which is used to correct the distance between the explosion center and the spatial unit. The position difference is obtained by comparing the coordinates of the old and new targets, and the result is the target placement offset.

[0119] Site space fine-tuning data refers to the modified parameters of obstacles and spatial layout of the test site in the new round of testing, which are used to re-screen candidate spatial units. Modification data is obtained by comparing the site parameters with historical site parameters through site retesting, and the retesting results are the site space fine-tuning data.

[0120] The first preset change rate threshold refers to the pre-set critical proportion of change in the amount of explosive charge, which serves as the criterion for determining the change in the shock wave range. The critical proportion of change is obtained by acquiring and analyzing historical data on the impact of explosive charge modifications on the shock wave range, and this critical proportion is the first preset change rate threshold.

[0121] The second preset rate of change threshold refers to the pre-set critical change ratio of the target body displacement, which serves as the criterion for determining the displacement of the explosion center. The critical change ratio is obtained by acquiring and analyzing historical data on the impact of target body displacement on the explosion center, and this critical ratio is the second preset rate of change threshold.

[0122] The third preset change rate threshold refers to the pre-set critical change ratio of site alteration, which serves as the criterion for judging changes in site spatial conditions. The critical change ratio is obtained by acquiring and analyzing historical data on the impact of site alteration on layout conditions, and this critical ratio is the third preset change rate threshold.

[0123] Reference Figure 1 As shown in some embodiments of this application, a multi-dimensional sensor adaptive layout method for an explosive cutting experiment includes: Step 1: Synchronously collect relevant data on explosive cutting conditions, target, charge, and site space. Collect various types of data to form parameter sets. After integrating and summarizing the parameter sets, establish a parameter ledger and retain the original records. Step 2: Determine the experimental range by retrieving the site spatial parameters from the parameter ledger, divide the entire area into multiple independent spatial units, number, measure coordinates and register information for each of the multiple independent spatial units, generate a spatial unit registration form and bind it to the previous parameter ledger; Step 3: Combine the space unit registration form and sensor installation requirements to compile a category matching list, and bind the sensors to the corresponding space units one by one; integrate all the data from the ledger, registration form and matching list to construct the point optimization calculation rule, calculate and filter to obtain the optimal deployment point, complete the on-site installation of the sensors with reference to the point information, and simultaneously generate a point deployment record table and associate it with all front-end forms; Step 4: Based on the point deployment record table, connect the sensors to carry out routine status and signal monitoring, extract offset data, compare with the original coordinates to complete point correction, and update various related forms simultaneously; integrate all sensor signals, time information and ledger data, complete data organization and archiving by category and time sequence, and realize data closed-loop flow by relying on the full-link related forms.

[0124] Specifically, when dividing the entire domain into multiple independent spatial units, this includes: The defined experimental area is divided into three-dimensional grids, and each grid node is used as a candidate spatial unit. Based on the obstacle distribution, signal obstruction estimate, and safety distance requirements in the site spatial parameters, the candidate spatial units are initially filtered to eliminate units that do not meet the installation feasibility. The remaining units are determined as independent spatial units, and numbering and information registration are completed based on the three-dimensional coordinates.

[0125] This can be understood as follows: the defined experimental area is divided into three-dimensional grids, discretizing the continuous three-dimensional experimental space into standardized grid nodes. This achieves quantitative representation and computability of the entire space, providing a unified spatial benchmark for subsequent site optimization calculations. Each grid node is used as a candidate spatial unit, establishing a one-to-one mapping relationship between spatial location and deployment feasibility. Nodes that are physically inaccessible are eliminated based on obstacle distribution in the site's spatial parameters; nodes with excessive signal transmission loss are excluded based on signal obstruction estimates; and nodes with excessive risk of explosion damage are screened out based on safety distance requirements. Through preliminary filtering using multi-dimensional constraints, independent spatial units with installation feasibility are retained. These units are numbered and registered based on three-dimensional coordinates, constructing a structured spatial unit database to support subsequent sensor grading and matching, and site optimization calculations.

[0126] For example, a rectangular area measuring 50m long, 40m wide, and 10m high was designated as the experimental area. A 1m×1m×1m cubic mesh was used to subdivide the experimental area into three dimensions, generating 23,001 mesh nodes as candidate spatial units. The explosion cutting center was preset at the three-dimensional coordinates (25,20,1). In the site spatial parameters, the obstacles consisted of three concrete support columns with a diameter of 0.8m and a height of 8m, with spatial ranges of (10,15,0)~(10,15,8), (25,20,0)~(25,20,8), and (40,25,0)~(40,25,0). 8) The signal obstruction estimate is set as ≥30% signal attenuation within 1m behind the obstacle, and the safety distance requirement is set as ≥5m from the explosion cutting center. All candidate spatial units are initially filtered to remove units that do not meet the installation feasibility requirements, are located within the obstacle entity, have a signal obstruction estimate ≥30%, or are less than 5m from the explosion cutting center. The remaining 18,726 units are determined as independent spatial units. They are numbered according to the three-dimensional coordinates of each unit using the "XYZ" format. At the same time, the obstacle distance, signal obstruction estimate, and distance from the explosion cutting center of each unit are recorded to generate a spatial unit registration table.

[0127] Specifically, when compiling a category matching list by combining the aforementioned space unit registration form and sensor installation requirements, the following is included: A first type of sensor matching priority, a second type of sensor matching priority, and a third type of sensor matching priority are preset, wherein the first type of sensor matching priority is higher than the second type of sensor matching priority, and the second type of sensor matching priority is higher than the third type of sensor matching priority; Based on the sensor's signal type, measurement range, and protection level, sensors are classified into key sensing, general monitoring, and auxiliary acquisition categories. When the space unit is located within the predicted shock wave overpressure peak area, the space unit is bound to a key sensing type sensor and the first type of sensor is assigned a matching priority. When the space unit is located outside the estimated shock wave overpressure peak area and within the coverage area of ​​the fragment scattering trajectory, the space unit is bound to a general monitoring sensor and a second type of sensor is assigned a matching priority. When a space unit is located outside the estimated shock wave overpressure peak area and outside the coverage area of ​​the fragment scattering trajectory, the space unit is bound to an auxiliary acquisition sensor and a third type of sensor is assigned a matching priority.

[0128] This can be understood as pre-setting a three-tiered, progressive sensor matching priority system to construct a hierarchical resource allocation decision-making system, providing a weighted benchmark for subsequent site optimization. Sensors are categorized into three classes based on their signal acquisition type, measurement range coverage, and environmental protection level, establishing a mapping relationship between sensor technical characteristics and the requirements of the explosion environment. Explosion fields exhibit significant spatial gradient distribution characteristics; the peak overpressure region of the shock wave has the highest requirements for sensor range and protection performance, followed by the fragmentation trajectory coverage, with the outer areas having the lowest requirements. Accordingly, different types of sensors are bound to the corresponding spatial units of the explosion-affected zones, and matching priorities are assigned simultaneously, achieving precise matching between sensor performance and regional monitoring needs.

[0129] For example, the matching priority is pre-set to 3 for the first type of sensor, 2 for the second type, and 1 for the third type, with higher priority values ​​indicating higher matching priority. Based on the sensor's signal type, measurement range, and protection level, the sensors are divided into three categories. The key sensing category is a piezoelectric shock wave pressure sensor, with a shock wave pressure signal, a measurement range of 0~10MPa, an IP68 protection level, and an impact resistance ≥1000g. The general monitoring category is a combination of a piezoelectric vibration sensor and a fragment velocity sensor, with vibration acceleration and fragment velocity signals, measurement ranges of 0~500g and 0~2000m / s respectively, an IP67 protection level, and an impact resistance ≥500g. The auxiliary acquisition category is a combination of an environmental temperature and humidity sensor and a sound level meter, with temperature and humidity signals and noise signals, measurement ranges of -40℃~85℃ and 0~140dB respectively. With an IP65 protection rating and an impact resistance of ≥100g, a spherical region with a radius of 5m to 10m is defined as the estimated peak value area of ​​the shock wave overpressure, and a spherical region with a radius of 10m to 30m is defined as the coverage area of ​​the fragment scattering trajectory. The area beyond a radius of 30m is defined as the outer perimeter area. All independent spatial units in the spatial unit registration table are traversed. Spatial units located within the estimated peak value area of ​​the shock wave overpressure are bound to key sensing sensors and assigned a first-class sensor matching priority. Spatial units located outside the estimated peak value area of ​​the shock wave overpressure but within the coverage area of ​​the fragment scattering trajectory are bound to general monitoring sensors and assigned a second-class sensor matching priority. Spatial units located outside the estimated peak value area of ​​the shock wave overpressure but outside the coverage area of ​​the fragment scattering trajectory are bound to auxiliary acquisition sensors and assigned a third-class sensor matching priority. All binding relationships are summarized to generate a category matching list.

[0130] Specifically, when constructing location optimization calculation rules by integrating all data from the aforementioned ledgers, registration forms, and matching lists, and calculating and filtering to obtain the optimal deployment locations, the following steps are included: Obtain the estimated sensor signal attenuation value, the distance between the spatial unit and the explosion cutting center, and the density of obstructions around the unit for each spatial unit with bound sensors; Spatial units whose sensor signal attenuation estimate is less than a preset attenuation threshold are marked as first preferred units, and spatial units whose sensor signal attenuation estimate is greater than or equal to the preset attenuation threshold are marked as second preferred units. Spatial units whose distance from the explosion cutting center is less than a preset safe distance threshold are marked as risk units; spatial units whose distance is greater than or equal to the preset safe distance threshold but less than the effective monitoring distance threshold are marked as effective monitoring units; and spatial units whose distance is greater than or equal to the effective monitoring distance threshold are marked as edge monitoring units. The first preferred unit further selects spatial units that are simultaneously marked as effective monitoring units as the optimal deployment points; When there is no spatial unit that simultaneously satisfies both the first preferred unit and the effective monitoring unit, the spatial units marked as effective monitoring units in the second preferred unit are selected as candidate deployment points. Based on the density values ​​of the surrounding obstructions, the spatial units with the lowest density values ​​are selected as the optimal deployment points.

[0131] Understandably, integrating multi-source data to construct a multi-dimensional quantitative evaluation system encompassing signal attenuation, spatial distance, and obstruction density enables standardized measurement of the value of spatial unit deployment. Based on signal transmission attenuation characteristics, first and second optimal units are divided, establishing a signal quality priority ranking mechanism. Combining the physical laws of the explosion field safety boundary and the effective detection radius of sensors, spatial units are divided into three categories: risk, effective monitoring, and edge monitoring, clarifying the spatial boundaries of deployment feasibility and data validity. Optimal deployment locations that simultaneously meet the requirements of high signal quality and effective monitoring are selected through the intersection operation of multiple constraints. When the optimal solution is missing, a suboptimal selection strategy based on obstruction density gradient ranking is adopted to select the relatively optimal deployment location from the second optimal unit, ensuring the integrity and robustness of the location selection process.

[0132] For example, by integrating all data from parameter ledgers, spatial unit registration forms, and category matching lists, a point optimization calculation rule is constructed. This calculation and filtering process yields the optimal deployment points. Specifically, this includes: obtaining the estimated sensor signal attenuation value for each spatial unit with bound sensors, the distance between the spatial unit and the explosion cutting center, and the density of obstructions around the unit. The estimated sensor signal attenuation value is calculated based on the type and distance of obstructions around the unit; the estimated attenuation value is 5% for unobstructed units, 12% for lightly obstructed units, and 25% for heavily obstructed units. The distance between the spatial unit and the explosion cutting center is calculated using a three-dimensional Euclidean distance formula. In the formula, d is the distance between the spatial unit and the explosion cutting center, and x, y, and z are the three-dimensional coordinates of the spatial unit. =25、 =20、 =1 represents the three-dimensional coordinates of the explosion cutting center. The density value of the obstruction around the unit is defined as the ratio of the projected area of ​​the obstruction within a 3m spherical range around the unit to the surface area of ​​the sphere, with a value range of 0~1. A preset attenuation threshold of 15%, a preset safe distance threshold of 5m, and an effective monitoring distance threshold of 30m are set, with a preset quantity of 20 units. Spatial units with a sensor signal attenuation prediction of less than 15% are marked as first preferred units, and spatial units with a sensor signal attenuation prediction of greater than or equal to 15% are marked as second preferred units. Spatial units with a distance of less than 5m from the explosion cutting center are marked as... For risk units, spatial units with a distance value greater than or equal to 5m and less than 30m are marked as effective monitoring units, and spatial units with a distance value greater than or equal to 30m are marked as edge monitoring units. In the first preferred unit, spatial units that are marked as effective monitoring units are further selected as the optimal deployment points. When there are no spatial units that simultaneously meet the requirements of the first preferred unit and the effective monitoring unit, the spatial units marked as effective monitoring units in the second preferred unit are selected as candidate deployment points, and the 20 spatial units with the lowest density values ​​are selected as the optimal deployment points according to the density values ​​of the surrounding obstructions from low to high.

[0133] Specifically, when performing routine status and signal monitoring of sensors based on the aforementioned point layout record table, and extracting offset data to compare with the original coordinates to complete point correction, the process includes: Obtain the real-time signal strength, signal time synchronization deviation, and physical location detection coordinates of each installed sensor; Sensors whose real-time feedback signal strength is lower than a preset signal strength threshold are marked as signal abnormal sensors; sensors whose signal time synchronization deviation is greater than a preset time deviation threshold are marked as timing abnormal sensors; and sensors whose physical position detection coordinates deviate from the original coordinates by a distance greater than a preset displacement deviation threshold are marked as displacement abnormal sensors. When any sensor is simultaneously marked as a signal abnormality sensor and a displacement abnormality sensor, it is determined that the sensor needs to perform physical position correction. A displacement correction vector is generated based on the difference between the physical position detection coordinates and the original coordinates. The installation coordinates of the sensor are re-determined based on the displacement correction vector, and the corresponding coordinate information in the point layout record table is updated. When any sensor is marked as a timing-abnormal sensor, it is determined that the sensor needs to perform parameter calibration, the local time base of the sensor is adjusted to synchronize with the experimental master clock, and the calibration timestamp is recorded in the associated form.

[0134] Understandably, a comprehensive sensor monitoring system is constructed by collecting multi-dimensional state parameters, covering three core operational indicators: signal transmission quality, timing synchronization accuracy, and physical installation location. Based on the correspondence between fault modes and parameter anomalies, a classification and judgment mechanism for signal anomalies, timing anomalies, and displacement anomalies is established to achieve accurate identification of different fault types. A multi-parameter joint judgment logic is employed, attributing faults involving simultaneous signal attenuation and positional shift to physical displacement. A displacement correction vector is generated through spatial vector operations to achieve quantitative correction of the installation location. Single timing anomalies are attributed to clock drift, and parameter calibration is completed through distributed system clock synchronization technology. Related forms and timestamp records are updated synchronously, constructing a fully traceable calibration management mechanism.

[0135] For example, based on the point deployment record, routine status and signal monitoring is conducted every 30 minutes for all installed sensors. Offset data is extracted and compared with the original coordinates to complete point correction. Specifically, this includes: obtaining the real-time signal strength of each installed sensor via a wireless gateway; comparing the sensor's local clock with the experimental master clock using the NTP time synchronization protocol to obtain the signal time synchronization deviation; and obtaining the physical position detection coordinates of each sensor through on-site measurement with a total station. A preset signal strength threshold of -60dBm, a preset time deviation threshold of 10μs, and a preset displacement deviation threshold of 0.2m are set. Sensors with real-time signal strength below -60dBm are marked as signal abnormal sensors; sensors with a signal time synchronization deviation greater than 10μs are marked as timing abnormal sensors; and sensors with a deviation distance between their physical position detection coordinates and the original coordinates greater than 0.2m are marked as displacement abnormal sensors. The deviation distance is calculated using the three-dimensional Euclidean distance formula. In the formula, d is the deviation distance. , , To detect coordinates for physical location, , , The original installation coordinates of the sensor are used. When any sensor is simultaneously marked as a signal abnormality sensor and a displacement abnormality sensor, it is determined that the sensor needs to undergo physical position correction. A displacement correction vector is generated based on the difference between the detected physical position coordinates and the original coordinates. The sensor is reinstalled to its original coordinate position according to the displacement correction vector, and the corresponding coordinate information in the point layout record table is updated. When any sensor is marked as a time-abnormal sensor, it is determined that the sensor needs to perform parameter correction, the local time reference of the sensor is adjusted to synchronize with the experimental master clock with an accuracy of 1μs, and the correction timestamp is recorded in the associated form.

[0136] Specifically, when integrating all sensor signals, time information, and ledger data, organizing and archiving the data by category and time sequence, and achieving closed-loop data flow based on end-to-end related forms, the following is included: Acquire the shock wave arrival time data, pressure peak data, and vibration spectrum data collected by all sensors during the explosive cutting experiment, and obtain the spatial unit number and timestamp information corresponding to the above data; The shock wave arrival time data are categorized by spatial unit number to form the first data subset, the pressure peak data are categorized by spatial unit number to form the second data subset, and the vibration spectrum data are categorized by spatial unit number to form the third data subset. Extract the arrival time difference of each spatial unit in the first data subset, and mark and remove abnormal data whose arrival time difference exceeds the preset time window range; Extract the relative deviation between the pressure peak value and the theoretically predicted peak value of each spatial unit in the second data subset. When the relative deviation exceeds the preset deviation threshold, perform a reverse association query on the sensor category matching priority and point layout record corresponding to the spatial unit, generate a deviation analysis report and attach it to the archived data. The first, second, and third data subsets, after removing outliers, are merged in timestamp order to form a complete time-series data chain. This complete time-series data chain is then linked with multiple tables, including the parameter ledger, spatial unit registration form, category matching list, and site layout record form, to generate a closed-loop archive of experimental data.

[0137] Understandably, based on the spatiotemporal coupling characteristics of explosion experiment data, a one-to-one mapping relationship is established between sensor data and spatial unit numbers and timestamps to achieve unified identification of multi-source heterogeneous data. A structured data organization method based on spatial unit classification is adopted, dividing the collected data of different physical quantities into independent subsets to support subsequent multi-dimensional data quality verification. Relying on the time-distance physical laws of shock wave propagation, automatic identification and removal of time-series abnormal data is achieved by comparing the difference between measured and theoretical arrival times and using preset time window thresholds. Based on the principle of data traceability, the pressure peak deviation is inversely correlated with sensor matching priority and point deployment records to construct a mechanism for tracing the causes of abnormal data. Timestamp sorting technology is used to merge and verify the data subsets to form a continuous time-series data chain. Relational database multi-table association technology is used to integrate the entire process of form data, generating a closed-loop experimental data archive with a complete traceability chain.

[0138] For example, integrating all sensor signals, time information, and ledger data, and organizing and archiving the data according to category and time sequence, and achieving closed-loop data flow based on the full-link associated forms, specifically includes: acquiring shock wave arrival time data, pressure peak data, and vibration spectrum data collected by all sensors during the explosive cutting experiment through a high-speed data acquisition system, and simultaneously acquiring the spatial unit number (in “XYZ” format, such as “8-12-1”) and timestamp information with an accuracy of 1μs corresponding to the above data; classifying the shock wave arrival time data according to the spatial unit number to form the first data subset, and classifying the pressure peak data according to the spatial unit number to form the second data subset. The vibration spectrum data are categorized by spatial unit number to form a third data subset. The arrival time difference of each spatial unit in the first data subset is extracted. This arrival time difference is the difference between the measured shock wave arrival time and the theoretical shock wave arrival time. The theoretical shock wave arrival time is calculated by dividing the distance between the spatial unit and the explosion cutting center by the air propagation speed of the shock wave, 340 m / s. A preset time window is set to 40 μs before and after the theoretical shock wave arrival time. Abnormal data with arrival time differences exceeding this range are marked and removed. The relative deviation between the pressure peak value and the theoretically estimated peak value of each spatial unit in the second data subset is extracted. The relative deviation is calculated using the following formula: In the formula, δ is the relative deviation, and P 实 P represents the measured peak pressure. 理 To calculate the theoretical peak value based on a 1kg TNT equivalent charge and distance, a preset deviation threshold of 20% is set. When the relative deviation exceeds 20%, the sensor category matching priority (3, 2, 1) and location deployment record corresponding to the spatial unit are queried in reverse association. A deviation analysis report containing the deviation value, sensor information, and location coordinates is generated and attached to the archived data. The first, second, and third data subsets after removing abnormal data are merged in ascending order of timestamp to form a complete time-series data chain. The complete time-series data chain is then linked with the previously generated parameter ledger, spatial unit registration table, category matching list, and location deployment record table through a relational database to generate a closed-loop experimental data archive containing full-process data and traceability links.

[0139] Specifically, when extracting the arrival time difference of each spatial unit in the first data subset, and marking and removing abnormal data whose arrival time difference exceeds a preset time window range, the process includes: A first time window threshold, a second time window threshold, and a third time window threshold are preset, wherein the first time window threshold is less than the second time window threshold, and the second time window threshold is less than the third time window threshold. The theoretical shock wave arrival time window corresponding to the spatial unit is determined based on the distance between the spatial unit and the explosion cutting center. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is less than or equal to the first time window threshold, the data is marked as high-confidence data and retained. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is greater than the first time window threshold and less than or equal to the second time window threshold, the data is marked as acceptable data and retained, and a time deviation mark is attached when archiving. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is greater than the second time window threshold and less than or equal to the third time window threshold, the data is marked as questionable data. The system then checks whether the sensor corresponding to the space unit has undergone point calibration or parameter calibration. If calibration has been performed, the data is downgraded to acceptable data. If calibration has not been performed, the data is marked as data to be reviewed. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is greater than the threshold of the third time window, the data is marked as abnormal data and removed, and an abnormal event record is generated and associated with the spatial unit.

[0140] Understandably, based on the time-distance physical laws of shock wave propagation, a mapping relationship is established between the distance of spatial units and the theoretical shock wave arrival time window. A hierarchical data quality evaluation system is constructed using a three-level progressive time window threshold to achieve quantitative grading of data reliability. By comparing the absolute value of the difference between the measured arrival time and the center value of the theoretical time window, the data is divided into four levels: high reliability, acceptable, questionable, and abnormal. Sensor calibration history is introduced as an auxiliary judgment dimension to perform secondary screening of questionable data and correct normal time deviations caused by equipment calibration. Based on the outlier detection principle, data exceeding the maximum permissible time deviation is judged as invalid data and removed, while generating associated abnormal event records to construct a complete data quality control and traceability mechanism.

[0141] For example, a first time window threshold of 10 μs, a second time window threshold of 25 μs, and a third time window threshold of 40 μs are preset, satisfying a progressive relationship where the first time window threshold is less than the second time window threshold, and the second time window threshold is less than the third time window threshold; the theoretical shock wave arrival time window corresponding to the spatial unit is determined based on the distance between the spatial unit and the explosion cutting center, where the theoretical shock wave arrival time t 0= d / v, where t0 is the theoretical shock wave arrival time, d is the distance between the spatial unit and the explosion cutting center, and v is the propagation speed of the shock wave in the air, taken as 340 m / s. The theoretical shock wave arrival time window is [t0-40 μs, t0+40 μs], and its center value is the theoretical shock wave arrival time t0. Calculate the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window: Δt = |t0|v. 实-t0|, where t is the actual arrival time of the shock wave measured by the sensor. When Δt≤10μs, the data is marked as high-confidence data and retained; when 10μs<Δt≤25μs, the data is marked as acceptable data and retained, and a "time deviation 10~25μs" mark is added when archiving; when 25μs<Δt≤40μs, the data is marked as questionable data, and the point layout record table is checked in reverse to see if there is a historical mark of point calibration or parameter calibration for the sensor corresponding to the spatial unit. If there is a calibration history mark, the data is downgraded to acceptable data; if there is no calibration history mark, it is marked as data to be reviewed; when Δt>40μs, the data is marked as abnormal data and removed, and an abnormal event record containing the spatial unit number, measured arrival time, theoretical arrival time and difference is generated and associated with the archive of the corresponding spatial unit.

[0142] Specifically, when extracting the relative deviation between the peak pressure of each spatial unit in the second data subset and the theoretically estimated peak pressure, and when the relative deviation exceeds a preset deviation threshold, a reverse correlation query is performed on the sensor category matching priority and location deployment records corresponding to that spatial unit. This process generates a deviation analysis report and appends it to the archived data, including: Obtain the sensor category matching priority corresponding to the spatial unit, and obtain the calibration history mark of the spatial unit in the point deployment record table; When the relative deviation exceeds the preset deviation threshold and the sensor category matching priority is the first type of sensor matching priority, it is determined to be a key data deviation, and a first-level deviation analysis report is generated. The report includes the relative deviation value, the theoretical estimated peak value, the actual pressure peak value, and the detailed three-dimensional coordinates of the spatial unit. When the relative deviation exceeds the preset deviation threshold and the sensor category matching priority is the second type of sensor matching priority, it is determined to be a general data deviation. Further check whether the spatial unit has a calibration history mark. If it has a calibration history mark, generate a secondary deviation analysis report and suggest recalibrating the sensor. If it does not have a calibration history mark, generate a secondary deviation analysis report and suggest checking the sensor installation status. When the relative deviation exceeds the preset deviation threshold and the sensor category matching priority is the third type of sensor matching priority, it is determined to be auxiliary data deviation. Only a level 3 deviation analysis report is generated as reference information, and no verification suggestion is triggered.

[0143] Understandably, based on the principle of data value stratification, a mapping relationship is established between sensor category matching priority and deviation handling level, introducing calibration history markers as an auxiliary dimension for determining equipment status. A three-level deviation judgment system is adopted, directly linking the degree of data anomaly to sensor importance, enabling differentiated anomaly response strategies. Key data deviations correspond to the highest priority sensors, generating a Level 1 deviation analysis report containing complete core parameters, constructing a full-element traceability link for key data. General data deviations, combined with calibration history markers, distinguish between two fault modes: equipment calibration drift and abnormal installation status, outputting targeted handling suggestions. Auxiliary data deviations only generate reference Level 3 reports, optimizing processing resource allocation while ensuring data integrity, forming a tiered response and precise traceability deviation control mechanism.

[0144] For example, with a preset deviation threshold of 20%, the relative deviation between the peak pressure of each spatial unit in the second data subset and the theoretically predicted peak pressure is extracted. The formula for calculating the relative deviation is: δ=|P 实 -P 理 | / P 理 ×100%, where δ is the relative deviation, P 实 The measured peak pressure is Ptheoretical, which is the theoretically estimated peak pressure calculated based on a 1kg TNT equivalent charge and the distance to the spatial unit. When the relative deviation exceeds 20%, a reverse lookup is performed on the sensor category matching priority and the calibration history marker in the location deployment record table corresponding to the spatial unit. The sensor category matching priority is 3 for the first category, 2 for the second category, and 1 for the third category. The calibration history marker has two states: "present" and "absent". When the relative deviation exceeds 20% and the sensor category matching priority is 3, it is determined to be a critical data deviation, and a first-level deviation analysis report is generated. The report includes a relative deviation value of 22.5%, a theoretically estimated peak pressure of 0.8MPa, and an actual peak pressure of 0. 98MPa and detailed three-dimensional coordinates (8,12,1) of the spatial unit; when the relative deviation exceeds 20% and the sensor category matching priority is 2, it is determined to be a general data deviation. Further check whether the spatial unit has a calibration history mark. If it has a calibration history mark, a secondary deviation analysis report is generated and it is recommended to recalibrate the sensor. If it does not have a calibration history mark, a secondary deviation analysis report is generated and it is recommended to check the sensor installation status; when the relative deviation exceeds 20% and the sensor category matching priority is 1, it is determined to be an auxiliary data deviation. Only a tertiary deviation analysis report is generated as reference information, and no verification suggestion is triggered. All deviation analysis reports are attached to the archived data of the corresponding spatial unit.

[0145] Specifically, after generating the closed-loop archive of experimental data, the following is also included: The closed-loop archive of experimental data is linked to the parameter log of the next explosive cutting experiment. The optimal deployment points, sensor category matching records, and point calibration history in the experiment are used as prior knowledge and input into the point optimization calculation rules of the next experiment. When the similarity between the site spatial parameters of the next experiment and the site spatial parameters of the experiment is greater than the preset similarity threshold, the spatial unit number of the optimal layout point in the experiment is directly reused, skipping some of the screening steps in the point optimization calculation. When the similarity is less than or equal to the preset similarity threshold, the deviation analysis report in the experiment is used as a weighted correction factor to input the point optimization operation rule and adjust the priority score of each spatial unit.

[0146] This can be understood as follows: Based on the principle of knowledge transfer, historical experimental data is linked to a closed-loop archive and new experimental parameter ledgers. Optimal deployment locations, sensor matching records, and calibration history are transformed into prior knowledge and embedded into location optimization rules. Based on the principle of spatial similarity, the reusability of historical solutions is evaluated through site parameter similarity metrics. When the similarity exceeds a preset threshold, the historical optimal location is directly reused, simplifying the optimization process. Based on the principle of deviation feedback, when the similarity is below a threshold, historical deviation analysis reports are converted into weighted correction factors, dynamically adjusting the priority scores of each spatial unit to optimize location optimization results in new scenarios, achieving adaptive reuse and iterative optimization of historical experience.

[0147] For example, after generating the closed-loop archive of experimental data, the closed-loop archive of this experiment is bound to the parameter log of the next explosive cutting experiment. The 20 optimal deployment points selected in this experiment, the sensor category matching records corresponding to 18,726 spatial units, and 3 point calibration history records are used as prior knowledge and input into the point optimization operation rules of the next experiment. The similarity between the site spatial parameters of the next experiment and the site spatial parameters of this experiment is calculated. The similarity calculation formula is: S=(M / N)×100%, where S is the similarity of site spatial parameters, M is the number of site spatial parameter items that match in the two experiments, and N is the total number of site spatial parameter items. In this embodiment, the total number of site spatial parameter items N=4, which are the site length, width, and height. The similarity threshold for the distribution of height and obstacles is set at 90%. When the similarity S > 90%, the spatial unit numbers of the 20 optimal placement points in this experiment are directly reused, skipping the three-dimensional meshing and preliminary filtering steps of candidate spatial units in the point optimization calculation. When the similarity S ≤ 90%, the deviation analysis report in this experiment is converted into a weighted correction factor. The weighted correction factor for spatial units with a relative deviation of 20% to 30% is 0.9, the weighted correction factor for spatial units with a relative deviation > 30% is 0.8, and the weighted correction factor for spatial units with no deviation is 1.0. The weighted correction factor is input into the point optimization calculation rules, and the original priority score of each spatial unit is multiplied by the corresponding weighted correction factor to obtain the adjusted priority score.

[0148] Specifically, after linking the closed-loop archive of experimental data with the parameter log for the next explosive cutting experiment, it also includes: Obtain the pre-calibration parameters for the next explosive cutting experiment. The pre-calibration parameters include the charge amount adjustment value, the target arrangement offset, and the site space fine-tuning data. The pre-calibrated parameters are compared item by item with the corresponding parameters in the closed-loop archive of historical experimental data, and the rate of change of each parameter is calculated. When the absolute value of the rate of change of the charge adjustment value is greater than the first preset rate of change threshold, it is determined that the shock wave coverage area has changed. Based on the changed coverage area, the estimated shock wave overpressure peak area and fragment scattering trajectory coverage area are re-determined, and the spatial unit and sensor binding relationship in the category matching list are updated simultaneously. When the absolute value of the rate of change of the target arrangement offset is greater than the second preset rate of change threshold, it is determined that the explosion cutting center has been displaced. The distance values ​​between all independent spatial units in the spatial unit registration table and the explosion cutting center are corrected in reverse according to the displacement vector. The corrected distance values ​​are then input into the point optimization operation rules to recalculate the division of effective monitoring units and edge monitoring units. When the absolute value of the rate of change of the site space fine-tuning data is greater than the third preset rate of change threshold, it is determined that the distribution of site obstacles or signal obstruction conditions have changed, and the preliminary filtering step of candidate space units is re-executed to remove newly added units that do not meet the installation feasibility. The updated category matching list, spatial unit registration form, and point optimization calculation rules are integrated to generate an incremental layout adjustment scheme. In this scheme, only the spatial units and sensor binding relationships that have changed are marked, while the historical optimal placement points of the unchanged parts are retained, thus completing the adaptive and rapid iteration of the sensor layout.

[0149] Understandably, based on the causal relationship between parameter changes and explosion field characteristics, a mapping relationship is established between the pre-calibrated parameter change rate and the layout adjustment triggering conditions. According to the fundamental laws of explosion mechanics, changes in the charge amount directly alter the overpressure distribution of the shock wave and the fragmentation range, triggering the re-division of the explosion impact zones and the updating of sensor binding relationships. Based on the principle of spatial coordinate transformation, target placement offsets cause displacement of the explosion cutting center reference point, which in turn corrects the relative distance values ​​of all spatial units, re-dividing effective monitoring and edge monitoring units. Based on the principle of spatial feasibility judgment, fine-tuning of the site space changes the distribution of obstacles and signal obstruction conditions, triggering the re-filtering of candidate spatial units. Incremental update technology is employed, marking only the changed spatial units and binding relationships, while retaining the historically optimal deployment points for the unchanged parts, achieving adaptive and rapid iteration of sensor layout.

[0150] For example, after binding the closed-loop archive of the current experimental data with the parameter log of the next explosive cutting experiment, the pre-calibrated parameters for the next explosive cutting experiment are obtained. These pre-calibrated parameters include a charge adjustment of 0.2 kg, a target placement offset of 1.414 m, and site space fine-tuning data (adding a concrete support column with a diameter of 0.6 m and a height of 6 m). The pre-calibrated parameters are then compared item by item with the corresponding parameters in the historical experimental data closed-loop archive, and the rate of change for each parameter is calculated. The formula for calculating the charge change rate is as follows: The formulas are as follows: R1 = |ΔW / W0| × 100%, where R1 is the rate of change of charge, ΔW is the adjustment value of charge, and W0 = 1 kg is the historical experimental charge. The formula for calculating the rate of change of target offset is: R2 = |ΔL / L0| × 100%, where R2 is the rate of change of target offset, ΔL is the offset of target placement, and L0 = 64.03 m is the diagonal length of the experimental site. The formula for calculating the rate of change of site fine-tuning is: R3 = |ΔN / N0| × 100%, where R3 is the rate of change of site fine-tuning, ΔN is the change in charge, and ΔN is the adjustment value of charge. The number of site parameters is calculated, with N0=4 representing the total number of site spatial parameters. The first preset change rate threshold is set to 15%, the second to 2%, and the third to 5%. The calculated charge change rate R1 = 20% > 15%, indicating a change in the shock wave coverage area. Based on the changed 1.2kg TNT equivalent charge, the estimated shock wave overpressure peak area is redefined as a spherical region with a radius of 5.5m to 11m centered on the new explosion center. The fragmentation trajectory coverage area is... A spherical region with a radius of 11m to 33m was defined, and the binding relationships between 1247 spatial units and sensors in the category matching list were updated simultaneously. The target offset change rate R2 was calculated to be approximately 2.21% > 2%, indicating a displacement at the explosion cutting center. The target shifted from historical coordinates (25, 20, 1) to (26, 21, 1), generating a displacement vector (1, 1, 0). Based on this displacement vector, the distance values ​​between all 18726 independent spatial units in the spatial unit registration table and the explosion cutting center were corrected using the following formula: 2 In the formula, d' is the corrected distance value, and x, y, z are the three-dimensional coordinates of the spatial unit. =26、 =21、 =1 is the new coordinate of the explosion cutting center. The corrected distance value is input into the point optimization calculation rule to recalculate and divide the effective monitoring units and edge monitoring units. The calculated site fine-tuning change rate R3=25%>5% indicates that the site obstacle distribution has changed. The preliminary filtering step of the candidate spatial units is re-executed to remove 326 units that do not meet the installation feasibility in the range of the newly added obstacles and the signal obstruction area 1m behind them. The updated category matching list, spatial unit registration form and point optimization calculation rule are integrated to generate an incremental layout adjustment scheme. The scheme only marks the binding relationship of 1573 spatial units that have changed and retains the historical optimal deployment points of 4 areas that have not changed, thus completing the adaptive rapid iteration of the sensor layout.

[0151] In the above embodiments, adaptive sensor placement is achieved through parameter ledgers, three-dimensional spatial unit division, and hierarchical matching optimization, avoiding the randomness of manual placement. Online monitoring and calibration ensure the stability of sensor acquisition. Layered data verification and archiving construct a closed-loop data system. Historical experimental archives can also be used as prior knowledge to adaptively iterate subsequent placement schemes based on changes in parameters such as charge, target, and site. This effectively improves the accuracy of sensor placement and the reliability of experimental data, and reduces the workload of repetitive placement.

[0152] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0154] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0155] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-dimensional sensor adaptive layout method for explosive cutting experiments, characterized in that, include: Simultaneously collect data related to the explosive cutting conditions, target, charge, and site space. Collect various types of data to form parameter sets. After integrating and summarizing the parameter sets, establish a parameter ledger and retain the original records. The experimental range is delineated by retrieving the site spatial parameters from the parameter ledger, the entire area is divided into multiple independent spatial units, the multiple independent spatial units are numbered, coordinates are measured and information is registered, a spatial unit registration form is generated and bound to the previous parameter ledger; By combining the space unit registration form and sensor installation requirements, a category matching list is compiled, and the sensors are bound to the corresponding space units one by one; by integrating all the data from the ledger, registration form and matching list, a point optimization calculation rule is constructed, the optimal deployment point is calculated and filtered to obtain the optimal deployment point, the sensor is installed on site with reference to the point information, and a point deployment record table is generated and associated with all front-end forms. Based on the point deployment record table, the sensors are connected to carry out routine status and signal monitoring, the offset data is extracted and compared with the original coordinates to complete the point correction, and various related forms are updated synchronously; all sensor signals, time information and ledger data are integrated, and the data is sorted and archived by category and time sequence, and the data closed-loop flow is realized by relying on the full-link related forms.

2. The multi-dimensional sensor adaptive layout method in the explosive cutting experiment according to claim 1, characterized in that, When dividing the entire domain into multiple independent spatial units, the following are included: The defined experimental area is divided into three-dimensional grids, and each grid node is used as a candidate spatial unit. Based on the obstacle distribution, signal obstruction estimate, and safety distance requirements in the site spatial parameters, the candidate spatial units are initially filtered to eliminate units that do not meet the installation feasibility. The remaining units are determined as independent spatial units, and numbering and information registration are completed based on the three-dimensional coordinates.

3. The multi-dimensional sensor adaptive layout method in the explosive cutting experiment according to claim 2, characterized in that, When compiling a category matching list by combining the aforementioned space unit registration form and sensor installation requirements, the following should be included: A first type of sensor matching priority, a second type of sensor matching priority, and a third type of sensor matching priority are preset, wherein the first type of sensor matching priority is higher than the second type of sensor matching priority, and the second type of sensor matching priority is higher than the third type of sensor matching priority; Based on the sensor's signal type, measurement range, and protection level, sensors are classified into key sensing, general monitoring, and auxiliary acquisition categories. When the space unit is located within the predicted shock wave overpressure peak area, the space unit is bound to a key sensing type sensor and the first type of sensor is assigned a matching priority. When the space unit is located outside the estimated shock wave overpressure peak area and within the coverage area of ​​the fragment scattering trajectory, the space unit is bound to a general monitoring sensor and a second type of sensor is assigned a matching priority. When a space unit is located outside the estimated shock wave overpressure peak area and outside the coverage area of ​​the fragment scattering trajectory, the space unit is bound to an auxiliary acquisition sensor and a third type of sensor is assigned a matching priority.

4. The multi-dimensional sensor adaptive layout method in the explosive cutting experiment according to claim 3, characterized in that, When constructing location optimization rules by integrating all data from the aforementioned ledgers, registration forms, and matching lists, and calculating and filtering to obtain the optimal deployment locations, the following are included: Obtain the estimated sensor signal attenuation value, the distance between the spatial unit and the explosion cutting center, and the density of obstructions around the unit for each spatial unit with bound sensors; Spatial units whose sensor signal attenuation estimate is less than a preset attenuation threshold are marked as first preferred units, and spatial units whose sensor signal attenuation estimate is greater than or equal to the preset attenuation threshold are marked as second preferred units. Spatial units whose distance from the explosion cutting center is less than a preset safe distance threshold are marked as risk units; spatial units whose distance is greater than or equal to the preset safe distance threshold but less than the effective monitoring distance threshold are marked as effective monitoring units; and spatial units whose distance is greater than or equal to the effective monitoring distance threshold are marked as edge monitoring units. The first preferred unit further selects spatial units that are simultaneously marked as effective monitoring units as the optimal deployment points; When there is no spatial unit that simultaneously satisfies both the first preferred unit and the effective monitoring unit, the spatial units marked as effective monitoring units in the second preferred unit are selected as candidate deployment points. Based on the density values ​​of the surrounding obstructions, the spatial units with the lowest density values ​​are selected as the optimal deployment points.

5. The multi-dimensional sensor adaptive layout method in the explosive cutting experiment according to claim 4, characterized in that, When performing routine status and signal monitoring of sensors based on the aforementioned point layout record table, and extracting offset data to compare with the original coordinates to complete point correction, the following steps are included: Obtain the real-time signal strength, signal time synchronization deviation, and physical location detection coordinates of each installed sensor; Sensors whose real-time feedback signal strength is lower than a preset signal strength threshold are marked as signal abnormal sensors; sensors whose signal time synchronization deviation is greater than a preset time deviation threshold are marked as timing abnormal sensors; and sensors whose physical position detection coordinates deviate from the original coordinates by a distance greater than a preset displacement deviation threshold are marked as displacement abnormal sensors. When any sensor is simultaneously marked as a signal abnormality sensor and a displacement abnormality sensor, it is determined that the sensor needs to perform physical position correction. A displacement correction vector is generated based on the difference between the physical position detection coordinates and the original coordinates. The installation coordinates of the sensor are re-determined based on the displacement correction vector, and the corresponding coordinate information in the point layout record table is updated. When any sensor is marked as a timing-abnormal sensor, it is determined that the sensor needs to perform parameter calibration, the local time base of the sensor is adjusted to synchronize with the experimental master clock, and the calibration timestamp is recorded in the associated form.

6. The multi-dimensional sensor adaptive layout method in the explosive cutting experiment according to claim 5, characterized in that, When integrating all sensor signals, time information, and ledger data, and organizing and archiving the data according to category and time sequence, and achieving closed-loop data flow based on end-to-end related forms, the following is included: Acquire the shock wave arrival time data, pressure peak data, and vibration spectrum data collected by all sensors during the explosive cutting experiment, and obtain the spatial unit number and timestamp information corresponding to the above data; The shock wave arrival time data are categorized by spatial unit number to form the first data subset, the pressure peak data are categorized by spatial unit number to form the second data subset, and the vibration spectrum data are categorized by spatial unit number to form the third data subset. Extract the arrival time difference of each spatial unit in the first data subset, and mark and remove abnormal data whose arrival time difference exceeds the preset time window range; Extract the relative deviation between the pressure peak value and the theoretically predicted peak value of each spatial unit in the second data subset. When the relative deviation exceeds the preset deviation threshold, perform a reverse association query on the sensor category matching priority and point layout record corresponding to the spatial unit, generate a deviation analysis report and attach it to the archived data. The first, second, and third data subsets, after removing outliers, are merged in timestamp order to form a complete time-series data chain. This complete time-series data chain is then linked with multiple tables, including the parameter ledger, spatial unit registration form, category matching list, and site layout record form, to generate a closed-loop archive of experimental data.

7. The multi-dimensional sensor adaptive layout method in the explosive cutting experiment according to claim 6, characterized in that, When extracting the arrival time difference of each spatial unit in the first data subset, and marking and removing abnormal data whose arrival time difference exceeds the preset time window range, the process includes: A first time window threshold, a second time window threshold, and a third time window threshold are preset, wherein the first time window threshold is less than the second time window threshold, and the second time window threshold is less than the third time window threshold. The theoretical shock wave arrival time window corresponding to the spatial unit is determined based on the distance between the spatial unit and the explosion cutting center. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is less than or equal to the first time window threshold, the data is marked as high-confidence data and retained. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is greater than the first time window threshold and less than or equal to the second time window threshold, the data is marked as acceptable data and retained, and a time deviation mark is attached when archiving. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is greater than the second time window threshold and less than or equal to the third time window threshold, the data is marked as questionable data. The system then checks whether the sensor corresponding to the space unit has undergone point calibration or parameter calibration. If calibration has been performed, the data is downgraded to acceptable data. If calibration has not been performed, the data is marked as data to be reviewed. When the absolute value of the difference between the actual arrival time and the center value of the theoretical shock wave arrival time window is greater than the threshold of the third time window, the data is marked as abnormal data and removed, and an abnormal event record is generated and associated with the spatial unit.

8. The multi-dimensional sensor adaptive layout method in the explosive cutting experiment according to claim 7, characterized in that, When extracting the relative deviation between the peak pressure of each spatial unit in the second data subset and the theoretically estimated peak pressure, and when the relative deviation exceeds a preset deviation threshold, a reverse correlation query is performed on the sensor category matching priority and location deployment records corresponding to that spatial unit. This generates a deviation analysis report, which is then appended to the archived data. This includes: Obtain the sensor category matching priority corresponding to the spatial unit, and obtain the calibration history mark of the spatial unit in the point deployment record table; When the relative deviation exceeds the preset deviation threshold and the sensor category matching priority is the first type of sensor matching priority, it is determined to be a key data deviation, and a first-level deviation analysis report is generated. The report includes the relative deviation value, the theoretical estimated peak value, the actual pressure peak value, and the detailed three-dimensional coordinates of the spatial unit. When the relative deviation exceeds the preset deviation threshold and the sensor category matching priority is the second type of sensor matching priority, it is determined to be a general data deviation. Further check whether the spatial unit has a calibration history mark. If it has a calibration history mark, generate a secondary deviation analysis report and suggest recalibrating the sensor. If it does not have a calibration history mark, generate a secondary deviation analysis report and suggest checking the sensor installation status. When the relative deviation exceeds the preset deviation threshold and the sensor category matching priority is the third type of sensor matching priority, it is determined to be auxiliary data deviation. Only a level 3 deviation analysis report is generated as reference information, and no verification suggestion is triggered.

9. The multi-dimensional sensor adaptive layout method in the explosive cutting experiment according to claim 8, characterized in that, After generating the closed-loop archive of experimental data, it also includes: The closed-loop archive of experimental data is linked to the parameter log of the next explosive cutting experiment. The optimal deployment points, sensor category matching records, and point calibration history in the experiment are used as prior knowledge and input into the point optimization calculation rules of the next experiment. When the similarity between the site spatial parameters of the next experiment and the site spatial parameters of the experiment is greater than the preset similarity threshold, the spatial unit number of the optimal layout point in the experiment is directly reused, skipping some of the screening steps in the point optimization calculation. When the similarity is less than or equal to the preset similarity threshold, the deviation analysis report in the experiment is used as a weighted correction factor to input the point optimization operation rule and adjust the priority score of each spatial unit.

10. The multi-dimensional sensor adaptive layout method in the explosive cutting experiment according to claim 9, characterized in that, After linking the closed-loop archive of experimental data with the parameter log for the next explosive cutting experiment, it also includes: Obtain the pre-calibration parameters for the next explosive cutting experiment. The pre-calibration parameters include the charge amount adjustment value, the target arrangement offset, and the site space fine-tuning data. The pre-calibrated parameters are compared item by item with the corresponding parameters in the closed-loop archive of historical experimental data, and the rate of change of each parameter is calculated. When the absolute value of the rate of change of the charge adjustment value is greater than the first preset rate of change threshold, it is determined that the shock wave coverage area has changed. Based on the changed coverage area, the estimated shock wave overpressure peak area and fragment scattering trajectory coverage area are re-determined, and the spatial unit and sensor binding relationship in the category matching list are updated simultaneously. When the absolute value of the rate of change of the target arrangement offset is greater than the second preset rate of change threshold, it is determined that the explosion cutting center has been displaced. The distance values ​​between all independent spatial units in the spatial unit registration table and the explosion cutting center are corrected in reverse according to the displacement vector. The corrected distance values ​​are then input into the point optimization operation rules to recalculate the division of effective monitoring units and edge monitoring units. When the absolute value of the rate of change of the site space fine-tuning data is greater than the third preset rate of change threshold, it is determined that the distribution of site obstacles or signal obstruction conditions have changed, and the preliminary filtering step of candidate space units is re-executed to remove newly added units that do not meet the installation feasibility. The updated category matching list, spatial unit registration form, and point optimization calculation rules are integrated to generate an incremental layout adjustment scheme. In this scheme, only the spatial units and sensor binding relationships that have changed are marked, while the historical optimal placement points of the unchanged parts are retained, thus completing the adaptive and rapid iteration of the sensor layout.

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