Electronic detonator traceability management early warning method and system based on big data

Abstract

The invention provides an electronic detonator traceability management early warning method and system based on big data. According to the method, a supply chain topology network is initialized in an electronic detonator production link, and a bidirectional tracing link is formed; in the transportation and storage links, a wireless energy field coupling device is used for activating a detonator built-in sensing unit, and movement track data and environment fluctuation data are collected; deploying a collaborative reasoning engine, and carrying out space-time association on the moving track and the environment fluctuation data to generate a detonator state abnormity probability value; inputting the raw material quality data and the transportation vibration intensity data into a collaborative inference engine for cross-level feature fusion, and generating safety traceability features; and when the abnormal probability value and the security traceability feature form an associated abnormal combination, triggering a grading early warning instruction and freezing the operation authority of the associated node. According to the invention, the full-life-cycle safety traceability and the real-time risk collaborative early warning precision of the electronic detonator in the links of transportation, storage and blasting are improved.

Description

Technical Field

[0001] The present application relates to the technical field of detonator traceability management and early warning, and in particular to a method and system for electronic detonator traceability management and early warning based on big data. Background Art

[0002] In the field of civil blasting engineering, electronic detonators require continuous monitoring of their condition and environmental conditions throughout the entire process, from production to blasting. This is particularly true during critical stages such as transportation vibration, storage temperature and humidity fluctuations, and geological conditions within the blasting area. Existing technologies must meet three core requirements: first, non-contact data collection through metal packaging to avoid safety hazards associated with unpacking inspections; second, real-time fusion and analysis of multi-dimensional data from transportation vibration and environmental parameters; and third, a coordinated early warning mechanism for abnormal risks across production, logistics, and blasting operations.

[0003] The current monitoring solution is based on RFID tags and cloud-based data analysis: anti-metal RFID tags are implanted in the detonator casings, and fixed readers are installed in transport cars and warehouses to collect location information. Independent temperature and humidity sensors and vibration recorders are also installed, and data is uploaded to the cloud-based analysis platform via the 4G network. The platform compares data from each link using preset thresholds and sends alarm information to management personnel when anomalies are found.

[0004] This solution has three limitations: first, RFID tags rely on close-range reading and writing and are easily shielded by metal packaging, resulting in data collection interruptions during transportation; second, independent sensors are separated from RFID system data, and the cloud platform can only perform simple threshold comparisons and cannot integrate multi-dimensional data such as vibration, temperature, humidity, and location to establish a dynamic risk model; third, the early warning mechanism lacks cross-link coordination capabilities, and the correlation analysis between material defects in the production link and transportation vibration data is missing, making it difficult to timely locate the hidden risk chain before blasting operations. Summary of the Invention

[0005] The present application provides a big data-based electronic detonator traceability management and early warning method and system to solve the problems of insufficient full life cycle safety traceability capability and real-time risk collaborative early warning accuracy of electronic detonators in the existing technology.

[0006] In a first aspect, the present application provides a big data-based electronic detonator traceability management and early warning method, comprising: Initializing a supply chain topology network with a hierarchical connection relationship in the electronic detonator production link, the supply chain topology network including raw material supply nodes, production and processing nodes, warehousing nodes, transportation nodes and blasting operation nodes; When the electronic detonator enters the transportation and warehousing stage, the detonator's built-in sensor unit is activated in a non-contact state through a pre-installed wireless energy field coupling device. The wireless energy field coupling device penetrates the packaging material based on the principle of electromagnetic resonance and collects the movement trajectory and environmental fluctuation data generated by the activated detonator's built-in sensor unit; Deploy a collaborative reasoning engine at an edge computing node near the blasting operation area to perform spatiotemporal correlation between the movement trajectory and the environmental fluctuation data, and combine the correlated data with the topographic and geological parameters of the blasting operation node to generate a detonator state abnormality probability value; Based on the hierarchical connection relationship of the supply chain topology network, the quality data of the raw material supply nodes and the vibration intensity data of the transportation link are input into the collaborative reasoning engine for cross-hierarchical feature fusion to generate a safety traceability feature containing multi-dimensional risk indicators; When the abnormal probability value of the detonator state forms an associated abnormal combination with the position offset index and the environmental mutation index in the safety traceability feature, a hierarchical early warning instruction is triggered and the operation authority of the associated node in the supply chain topology network is frozen.

[0007] Optionally, based on the hierarchical connection relationship of the supply chain topology network, the quality data of the raw material supply nodes and the vibration intensity data of the transportation link are input into the collaborative reasoning engine for cross-hierarchical feature fusion to generate a safety traceability feature containing multi-dimensional risk indicators, including: Obtaining metal purity test records of raw material supply nodes and physical vibration intensity peaks of transportation nodes based on the hierarchical paths of the supply chain topology network; In the collaborative reasoning engine, the metal purity detection records are numbered by production batch, and the physical vibration intensity peak is coded by transportation period, and then cross-node correlation matching is performed; A safety traceability feature subset including material strength index and vibration tolerance index is generated by associating the matching results, and the safety traceability feature subset is merged with the position offset index and the environmental mutation index to form a complete safety traceability feature.

[0008] Optionally, a collaborative reasoning engine is deployed at an edge computing node near the blasting operation area to perform spatiotemporal correlation between the movement trajectory and the environmental fluctuation data, and a detonator state abnormality probability value is generated by combining the correlated data with the topographic and geological parameters of the operation node of the blasting operation node, including: A collaborative reasoning engine is started at an edge computing node near the blasting operation area to receive the position coordinate sequence from the movement trajectory data and the temperature and humidity change curve from the environmental fluctuation data. Synchronize and align the timestamp of the position coordinate sequence with the acquisition time of the temperature and humidity change curve to form a time-space bound position and environmental status record set; The location and environmental status record set and the terrain slope value and rock hardness value of the blasting operation area are input into the collaborative reasoning engine, and a detonator state abnormality probability value representing the current safety state of the detonator is output.

[0009] Optionally, when the electronic detonator enters the transportation and warehousing stage, a preset wireless energy field coupling device is used to activate the detonator's built-in sensing unit in a non-contact state. The wireless energy field coupling device penetrates the packaging material based on the principle of electromagnetic resonance and collects the movement trajectory and environmental fluctuation data generated by the activated built-in sensing unit of the detonator, including: Multiple wireless energy field coupling devices are deployed at preset locations on the top of transport carriages and storage shelves. When electronic detonators enter the electromagnetic coverage area, they transmit energy to the detonator's built-in sensor unit through the packaging material through the principle of electromagnetic resonance, activating the detonator's built-in sensor unit to switch from sleep mode to working mode. The activated detonator's built-in sensor unit continuously records the three-dimensional spatial displacement coordinate sequence, the temperature and humidity change curve inside the package, and the physical vibration intensity waveform transmitted from the outside; The three-dimensional spatial displacement coordinate sequence, the temperature and humidity change curve inside the package, and the physical vibration intensity waveform transmitted from the outside are transmitted to local data nodes in the transport compartment and storage shelves through near-field communication, and combined to form movement trajectory data and environmental fluctuation data.

[0010] Optionally, when the abnormal probability value of the detonator state forms an associated abnormal combination with the position offset index and the environmental mutation index in the safety traceability feature, triggering a graded early warning instruction and freezing the operation authority of the associated node in the supply chain topology network includes: Real-time monitoring of the abnormal probability value of the detonator state, the position offset value in the safety traceability feature, and the amplitude value of the environmental mutation; When the probability value of the detonator state abnormality continuously exceeds the probability threshold, and the position offset value exceeds the displacement allowable value, and the environmental mutation amplitude value exceeds the mutation allowable value, it is determined that an associated abnormal combination is formed; According to the abnormal indicator type of the associated abnormal combination, a cargo inspection instruction is sent to the transportation node and an operation interruption instruction is sent to the blasting node, and the digital operation authority of the corresponding node in the supply chain topology network is simultaneously blocked.

[0011] Optionally, in the collaborative reasoning engine, the metal purity detection records are numbered by production batch, and the physical vibration intensity peak is encoded by transportation period, and then cross-node correlation matching is performed, including: A unique production batch number is attached to each metal purity test record of the raw material supply node, and the production batch number includes a combination of the supplier code and the production date; Add a transportation period code to the peak value data of physical vibration intensity recorded during the transportation process. The transportation period code consists of the starting loading and unloading point location code and the transportation time interval. In the collaborative reasoning engine, based on the node connection relationship of the supply chain topology network, the metal purity detection records of the same production batch number and the physical vibration intensity peak values ​​coded in the same transportation period are cross-node associated and matched.

[0012] Optionally, synchronizing the timestamps of the position coordinate sequence with the acquisition time of the temperature and humidity change curve to form a spatiotemporally bound position and environmental status record set includes: Assigning a first unified timestamp to each coordinate point in the position coordinate sequence, where the first unified timestamp is derived from a timing clock of the edge computing node; Assigning a second unified timestamp to each monitored value of the temperature and humidity change curve, wherein the second unified timestamp and the first unified timestamp use the same time reference; According to the overlapping interval of the first unified timestamp and the second unified timestamp, the coordinate points of the position coordinate sequence are paired and combined with the monitoring values ​​of the temperature and humidity change curve to form a time-space bound position and environmental status record set.

[0013] Secondly, this application provides an electronic detonator traceability management and early warning system based on big data, including: An initialization module is used to initialize a supply chain topology network with a hierarchical connection relationship in the electronic detonator production link, wherein the supply chain topology network includes a raw material supply node, a production and processing node, a storage node, a transportation node, and a blasting operation node; An activation module is used to activate the detonator's built-in sensor unit in a non-contact manner when the electronic detonator enters the transportation and warehousing stages through a pre-installed wireless energy field coupling device. The wireless energy field coupling device uses the principle of electromagnetic resonance to penetrate the packaging material and collect the movement trajectory and environmental fluctuation data generated by the activated detonator's built-in sensor unit; A generation module is used to deploy a collaborative reasoning engine at an edge computing node near the blasting operation area, perform spatiotemporal correlation between the movement trajectory and the environmental fluctuation data, and combine the correlated data with the operation node topographic and geological parameters of the blasting operation node to generate a detonator state abnormality probability value; An input module is used to input the quality data of raw material supply nodes and the vibration intensity data of the transportation link into the collaborative reasoning engine based on the hierarchical connection relationship of the supply chain topology network to perform cross-hierarchical feature fusion and generate a safety traceability feature containing multi-dimensional risk indicators; The trigger module is used to trigger a hierarchical early warning instruction and freeze the operation authority of the associated node in the supply chain topology network when the abnormal probability value of the detonator state forms an associated abnormal combination with the position offset index and the environmental mutation index in the safety traceability feature.

[0014] In a third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a big data-based electronic detonator traceability management and early warning method as described in the first aspect above.

[0015] In a fourth aspect, the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements an electronic detonator traceability management and early warning method based on big data as described in the first aspect.

[0016] In the embodiment of the present application, a supply chain topology network with a hierarchical connection relationship is initialized in the electronic detonator production link, and the supply chain topology network includes raw material supply nodes, production and processing nodes, warehousing nodes, transportation nodes and blasting operation nodes; when the electronic detonator enters the transportation and warehousing links, the built-in sensor unit of the detonator is activated in a non-contact state through a preset wireless energy field coupling device. The wireless energy field coupling device penetrates the packaging material based on the principle of electromagnetic resonance and collects the movement trajectory and environmental fluctuation data generated by the activated built-in sensor unit of the detonator; a collaborative reasoning engine is deployed at the edge computing node near the blasting operation area to convert the movement trajectory and environmental fluctuation data into the data of the environment fluctuation. The trajectory is temporally and spatially correlated with the environmental fluctuation data, and the correlated data is combined with the topographic and geological parameters of the operation node of the blasting operation node to generate a detonator status abnormality probability value; based on the hierarchical connection relationship of the supply chain topology network, the quality data of the raw material supply node and the vibration intensity data of the transportation link are input into the collaborative reasoning engine for cross-hierarchical feature fusion to generate a safety traceability feature containing multi-dimensional risk indicators; when the detonator status abnormality probability value forms an associated abnormal combination with the position offset indicator and the environmental mutation indicator in the safety traceability feature, a hierarchical early warning instruction is triggered and the operation authority of the associated node in the supply chain topology network is frozen.

[0017] The technical solution of this application has the following beneficial effects: Establish a digital traceability foundation for electronic detonators, from raw material supply to blasting operations, to accurately locate the responsible parties at each node in the lifecycle. Break through the metal packaging barrier to obtain contactless access to the core status data of detonators, avoiding the risk of unpacking inspections and ensuring continuous monitoring of transportation and warehousing. Integrate movement trajectories, environmental fluctuations, and geological parameters of the blasting area to output a real-time quantitative assessment of the safety status of detonators adapted to complex terrain. Break through the topological hierarchical barriers between raw material quality and transportation vibration data to construct a multidimensional risk fingerprint reflecting material strength and vibration tolerance. Based on the coordinated deviation determination of abnormal probability values ​​and location / environmental indicators, achieve precise hierarchical control and authority freezing before blasting operations.

[0018] Furthermore, based on the supply chain topology hierarchical path, raw material metal purity records and transport vibration intensity peaks are obtained. In the collaborative reasoning engine, cross-node correlation matching is performed based on production batch numbers and transport period codes. This generates a safety traceability feature subset containing material strength indicators and vibration tolerance indicators. Position offset and environmental mutation indicators are combined to form a complete safety traceability feature. A topological correlation chain is established between raw material production batch and transport period data, revealing the hidden risks caused by the coupling of material defects and transport vibration. This expands the safety traceability feature's assessment dimension of detonator structural tolerance and improves the completeness of pre-blasting risk prediction.

[0019] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 A flowchart of a big data-based electronic detonator traceability management and early warning method provided by the present application is shown; Figure 2 A scenario diagram showing a method for tracing and managing early warning of electronic detonators based on big data provided by the present application is shown; Figure 3 The figure shows a schematic diagram of the structure of an electronic detonator traceability management and early warning system based on big data provided by the present application; Figure 4 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0023] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0024] Existing electronic detonator monitoring solutions rely on anti-metal RFID and cloud-based threshold analysis, which presents three technical bottlenecks: First, the shielding effect of metal packaging leads to data collection interruptions during transportation, making continuous monitoring of the entire process impossible; second, the independent sensor system is separated from the RFID data, and the cloud platform only supports single-dimensional threshold alarms and lacks the dynamic fusion and analysis capabilities of vibration, temperature, humidity, and location data; third, the early warning mechanism is isolated between the production, transportation, and blasting links, and the correlation chain between raw material quality defects and transportation vibration data is broken, making it difficult to trace and locate hidden risks before blasting operations.

[0025] In response to the above-mentioned defects, this application proposes a full life cycle management method that integrates supply chain topology relationship chain, wireless energy field coupling and edge computing collaborative reasoning. Its core breakthrough lies in: using the principle of electromagnetic resonance to drive the wireless energy field coupling device to penetrate the metal packaging to activate the built-in sensor unit, directly obtain the movement trajectory and environmental fluctuation data, and eliminate the blind spot of transportation link monitoring; deploy edge computing nodes in the blasting area, and associate the movement trajectory, environmental fluctuation data with topographic and geological parameters in real time to generate dynamic abnormality probability values, and solve the problem of multi-dimensional data islands; based on the hierarchical relationship of the supply chain topology network, the raw material quality data and the transportation vibration intensity are correlated and matched across nodes, and a safety traceability feature containing material strength and vibration tolerance indicators is constructed to realize risk transmission modeling of production, transportation, and blasting links; when the abnormal probability value forms a combined deviation with the position offset and environmental mutation indicators, a hierarchical warning is automatically triggered and the operation authority is frozen, and a cross-link collaborative response mechanism is established.

[0026] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0027] Figure 1 A flowchart of a method for tracing and managing early warning of electronic detonators based on big data is provided for the embodiment of the present application. Figure 1 As shown, the method includes: 101. Initializing a supply chain topology network with a hierarchical connection relationship in the electronic detonator production link, wherein the supply chain topology network includes a raw material supply node, a production and processing node, a storage node, a transportation node, and a blasting operation node; In the above solution, the supply chain topology network uses digital relationships to simulate the entire process path of electronic detonators, from raw materials to blasting. It includes five types of nodes: raw material supply nodes, production and processing nodes, warehousing nodes, transportation nodes, and blasting operation nodes. A digital identity code is a unique identification code generated for each electronic detonator, which is used to link the data of these nodes to form a two-way traceability chain.

[0028] In an embodiment of the present application, first, five types of node database tables are created in the electronic detonator production management system, and data fields are defined for each type of node. For example, the raw material node table contains the "supplier number" and "metal purity value" fields, and the production node table contains the "production line number" and "assembly completion time" fields.

[0029] Secondly, establish node connection rules: when the production node receives raw material node data, the system automatically binds the raw material batch number with the detonator production batch number; when the storage node receives production node data, it binds the detonator number with the warehouse shelf location, and so on until the blasting node.

[0030] Next, a digital identity code is generated for each detonator. This code, consisting of the production date, production line number, and serial number, is written into the detonator's built-in chip. Node operation permissions are then configured: production nodes can edit raw material data, transportation nodes can only upload vibration data, and blasting nodes can only associate geological parameters, ensuring data accountability.

[0031] Finally, verify the connectivity of the topological network: simulate the test data from the raw material node to the production node to the transportation node, and finally generate a complete traceability chain at the blasting node.

[0032] 102. When the electronic detonator enters the transportation and storage stage, the built-in sensor unit of the detonator is activated in a non-contact state through a pre-installed wireless energy field coupling device. The wireless energy field coupling device penetrates the packaging material based on the principle of electromagnetic resonance and collects the movement trajectory and environmental fluctuation data generated by the activated built-in sensor unit of the detonator; Optionally, step 102 may specifically include the following steps: 1021. Deploy multiple wireless energy field coupling devices at preset locations on the top of transport carriages and storage shelves. When electronic detonators enter the electromagnetic coverage area, they transmit energy to the detonator's built-in sensor unit through the packaging material using the principle of electromagnetic resonance, activating the detonator's built-in sensor unit to switch from sleep mode to working mode. 1022. The activated built-in sensor unit of the detonator continuously records the three-dimensional spatial displacement coordinate sequence, the temperature and humidity change curve inside the package, and the physical vibration intensity waveform transmitted from the outside; 1023. Transmit the three-dimensional spatial displacement coordinate sequence, the temperature and humidity change curve inside the package, and the externally transmitted physical vibration intensity waveform to local data nodes in the transport compartment and storage shelf via near-field communication, and combine them to form movement trajectory data and environmental fluctuation data.

[0033] In the above solution, the wireless energy field coupling device is a transmitter mounted on the roof or shelf of a train car. It transmits a magnetic field of a specific frequency through the wooden packaging box, wirelessly powering the chip built into the detonator. The detonator's built-in sensing unit is a miniature sensor within the electronic detonator. It consumes zero power when dormant and continuously records position, temperature, humidity, and vibration data when activated. The three-dimensional spatial displacement coordinate sequence is a time series of position coordinates generated every second by the positioning chip built into the sensing unit. The physical vibration intensity waveform is a waveform graph of the vibration intensity over time, captured by the sensing unit's accelerometer, reflecting the degree of transport turbulence. The local data node is a small data receiver within the train car or warehouse that temporarily stores sensor data and forwards it to the edge computing node.

[0034] In this embodiment, first, in step 1021, three sets of wireless energy field coupling devices are installed on the metal frame atop the transport truck compartment, with one device spaced two meters apart to form a continuous coverage area. When a wooden crate containing an electronic detonator is loaded onto the compartment, the device automatically detects the crate's position and emits a 6.78 MHz high-frequency electromagnetic field. This field penetrates the 5-centimeter-thick wooden crate, resonating with the detonator's internal receiving coil, converting the electromagnetic energy into electrical energy and activating the sensor unit from its dormant state.

[0035] Subsequently, in step 1022, the activated sensor unit activates three detection modules: the positioning module acquires satellite positioning coordinates every second to form a displacement sequence, the temperature and humidity sensor records the internal values ​​of the packaging box every minute to form a change curve, and the high-precision accelerometer captures the vibration energy transmitted from the car floor at a 100Hz sampling rate to form a waveform. These three sets of data are synchronized with timestamps by the sensor unit's internal processor to ensure that all records maintain the same time reference.

[0036] Finally, in step 1023, the sensor unit activates the Bluetooth 5.0 protocol and transmits the compressed coordinate sequence, temperature and humidity curve, and vibration waveform to a local data node within a 15-meter range. Upon receiving the data, the node performs format conversion: organizing the coordinate sequence into a path trajectory table, combining the temperature and humidity curve and vibration waveform into an environmental monitoring table, and finally packaging it into a complete data set and adding the electronic detonator identification.

[0037] In actual application, a civil explosives company, while transporting 200 electronic detonators, installed four energy field devices on the roof of a van, according to step 1021. When a detonator box carrying the number TP-2024-015 entered the van, the device detected the metal response and emitted a 13.56MHz magnetic field that penetrated the pine wood paneling, activating the detonator sensor units inside the box.

[0038] Step 1022: The activated sensor unit starts working. The GPS module records the position every 3 seconds (118.78°E, 32.04°N→118.79°E, 32.05°N). The temperature and humidity sensors collect data every 30 seconds. The temperature rises from 25°C to 38°C, and the humidity drops from 60% to 45%. The three-axis accelerometer captures the 0.6G shock waveform generated when the vehicle passes over the speed bump in real time.

[0039] In step 1023, the sensor unit transmits 12,000 location points, 960 sets of temperature and humidity records, and 2.8 million vibration data points generated during the 8-hour transport process in batches to the data node in the cab via a 2.4GHz wireless channel. The node connects the location points into a transport route map, converts the temperature and humidity records into a variation curve, and generates a power spectrum for the vibration data. These are combined into the "TP-2024-015 Transport Dataset." When the detonator enters the warehouse, the energy field device on the shelf reactivates the sensor unit. The temperature and humidity sensors continuously monitor the storage environment, maintaining a constant temperature of 20°C ± 2°C and a humidity of 50% ± 5%. This data is transmitted in real time to the data node on the warehouse column, which updates the environmental fluctuation data record.

[0040] The overall solution of step 102 above realizes fully automatic status monitoring of electronic detonators in closed transportation and storage environments, breaks through the limitations of packaging barriers through non-contact energy transmission, obtains high-precision position trajectory and environmental parameters, and provides a complete, continuous and reliable link data foundation for collaborative management of the entire life cycle, completely eliminating the safety risks of traditional manual unpacking inspections.

[0041] 103. Deploy a collaborative reasoning engine at an edge computing node near the blasting operation area, perform spatiotemporal correlation between the movement trajectory and the environmental fluctuation data, and combine the correlated data with the topographic and geological parameters of the blasting operation node to generate a detonator state abnormality probability value; Optionally, step 103 may specifically include the following steps: 1031. Start a collaborative reasoning engine at an edge computing node near the blasting operation area to receive the position coordinate sequence in the movement trajectory data and the temperature and humidity change curve in the environmental fluctuation data; 1032. Synchronize and align the timestamp of the position coordinate sequence with the acquisition time of the temperature and humidity change curve to form a time-space bound position and environmental status record set; Among them, step 1032 may specifically include the following process: assigning a first unified timestamp to each coordinate point of the position coordinate sequence, the first unified timestamp is derived from the timing clock of the edge computing node; assigning a second unified timestamp to each monitoring value of the temperature and humidity change curve, the second unified timestamp and the first unified timestamp use the same time base; according to the timestamp overlapping interval of the first unified timestamp and the second unified timestamp, the coordinate points of the position coordinate sequence are paired and combined with the monitoring values ​​of the temperature and humidity change curve to form a time-space bound position and environmental status record set.

[0042] 1033. Input the location and environmental status record set, the terrain slope value, and the rock hardness value of the blasting operation area into the collaborative reasoning engine, and output a detonator status abnormality probability value representing the current safety status of the detonator.

[0043] In the above solution, edge computing nodes are ruggedized computer equipment deployed at the blasting site. The collaborative inference engine is a multi-source data fusion and analysis software module. The location coordinate sequence is a chronological collection of longitude, latitude, and altitude data. The temperature and humidity curve is a graph showing the continuous change in temperature and humidity over time. The terrain slope value is a measurement of the inclination angle of the blasting surface. The rock hardness value is a numerical classification of the compressive strength of the rock in the work area. The spatiotemporally bound record set is a combination of location and environmental data at the same point in time.

[0044] In this embodiment, first, in step 1031, an explosion-proof edge computing cabinet is built outside the blast warning area, and the equipment inside the cabinet is loaded with collaborative reasoning engine software. When the transport vehicle enters the 3-kilometer range of the operation area, the engine automatically receives two key data items through a dedicated data interface: a GPS coordinate sequence recorded throughout the electronic detonator transportation process, and a change curve generated by the temperature and humidity sensors inside the packaging box, which contains 480 temperature and humidity monitoring values.

[0045] Next, in step 1032, the time base is unified: the millisecond-accurate time stamp for each location in the coordinate sequence is extracted, and the temperature and humidity curve is searched for the monitored values ​​recorded at the same millisecond time. Successfully matched spatial locations and corresponding environmental values ​​are merged into a data unit, such as "E118.79°, N32.04°, 82m above sea level, 32.5°C, 45%, 2024-07-06 14:25:03.125." After all data are matched, a spatiotemporally bound record set is formed.

[0046] Finally, in step 1033, the bound record set and the site survey parameters are fed into the engine: the 35-degree slope value obtained by the topographic survey instrument and the granite hardness value output by the core tester, which is 7.5 on the Mohs scale. The engine automatically builds a correlation model between the transportation environment data and the geological conditions. For example, when a high-temperature transportation record peaks at 42°C and encounters a steep hard rock working surface, the engine calculates the risk of detonator structure failure and outputs an abnormal probability value of 0.93 on a scale of 0-1.

[0047] In actual application, the copper mine blasting project installed an edge computing node in a cave 200 meters outside the work area through step 1031 to receive the transportation data of the detonator numbered TP-8876: location coordinate sequence: 186 bend coordinates from the warehouse to the mining area, temperature and humidity curve: a jagged fluctuation graph of the temperature in the car rising from 18°C ​​to 39°C.

[0048] Through step 1032, the system uses the edge node atomic clock as a benchmark to align the two types of data time axes - binding the sharp turn coordinates E102.72°, N25.05° at 10:15:30 on July 5 to the temperature and humidity values ​​of 36.8℃ / 41% at the same millisecond, generating 1024 time-space binding records throughout the process.

[0049] In step 1033, the measured on-site parameters are input: a 40-degree slope and a gneiss hardness of 6.8 on the Mohs scale. The inference engine detects two overlapping risks: an increased expansion coefficient of explosives due to high temperature and increased impact force during steep slope operations, outputting an abnormality probability value of 0.89. When the probability value exceeds the 0.85 threshold, subsequent control procedures are automatically triggered: the blasting operation permission for the detonator in the supply chain topology network is frozen, and a "Detonator TP-8876 Structural Stability Warning" command is sent to the on-site manager.

[0050] The overall solution of step 103 above realizes the dynamic coupling analysis of transportation environment data and blasting geological parameters, quantifies the risk level of detonator operations in real time through edge computing, provides accurate safety predictions for high-risk scenarios, and breaks through the technical barriers of the disconnection between transportation monitoring and usage scenarios in traditional solutions.

[0051] 104. Based on the hierarchical connection relationship of the supply chain topology network, the quality data of the raw material supply nodes and the vibration intensity data of the transportation link are input into the collaborative reasoning engine for cross-hierarchical feature fusion to generate a safety traceability feature containing multi-dimensional risk indicators; Optionally, step 104 may specifically include the following steps: 1041. Obtain metal purity test records of raw material supply nodes and physical vibration intensity peak values ​​of transportation nodes based on the hierarchical path of the supply chain topology network; 1042. In the collaborative reasoning engine, the metal purity test records are numbered by production batch, and the physical vibration intensity peak values ​​are coded by transportation period, and then cross-node correlation matching is performed; Among them, step 1042 may specifically include the following processes: attaching a unique production batch number to each metal purity detection record of the raw material supply node, and the production batch number includes a combination of the supplier code and the production date; adding a transportation period code to the physical vibration intensity peak data recorded in the transportation link, and the transportation period code consists of the starting loading and unloading point location code and the transportation time interval; in the collaborative reasoning engine, based on the node connection relationship of the supply chain topology network, the metal purity detection records with the same production batch number and the physical vibration intensity peak with the same transportation period code are cross-node associated and matched.

[0052] 1043. Generate a safety traceability feature subset including material strength index and vibration tolerance index through correlation matching results, and merge the safety traceability feature subset with position offset index and environmental mutation index into a complete safety traceability feature.

[0053] In the above scheme, the hierarchical path is the pre-set data transmission route between supply chain nodes. The metal purity test record is the metal purity value detected when the raw materials are stored. The physical vibration intensity peak is the maximum vibration intensity value recorded during transportation. The production batch number is a unique identification code composed of the production date and production line number. The transportation period code is an identifier composed of the start and end time and location of the transportation task. The material strength index is a quantitative parameter reflecting the relationship between metal purity and vibration tolerance. The vibration tolerance index is a value that represents the detonator structure's ability to resist vibration.

[0054] In this embodiment, the system first automatically retrieves data along the supply chain topology network hierarchical path in step 1041. It extracts the metal purity test report for a specific supplier from the raw material node database, including the material type, test time, and purity percentage. It also simultaneously retrieves peak intensity data recorded by the vibration sensor from the transportation node log, including the vibration occurrence time, duration, and intensity value.

[0055] Subsequently, in step 1042, the collaborative reasoning engine performs dual-track encoding. Metal purity test records are numbered by production batch, using the "production date + production line number" format, such as 20240801-L03. Physical vibration intensity peaks are coded by transportation time period, using the "start time + end point code" format, such as 0801-15H_D07. Based on the topological network node connections, the engine automatically associates and matches production records from the same batch date with transportation data from the same time period.

[0056] Finally, in step 1043, feature indices are calculated based on the matching results. The material strength index is calculated by multiplying the metal purity value by the material's vibration resistance coefficient; the vibration tolerance index is calculated by multiplying the vibration peak intensity by the material attenuation factor. The resulting feature subset is combined with the position offset index from step 102 and the environmental mutation index from step 103 to form a four-dimensional safety traceability feature.

[0057] In actual application, through step 1041, a certain electronic detonator factory uses copper materials in the production batch B0821. The system extracts the purity test record of this batch of copper materials from the raw material node, numbered CU-0821, with a purity of 99.2%; and obtains the vibration peak data of this batch of detonators transported to the Shanxi mine on August 3 from the transportation node, vehicle V12, with a peak value of 1.05G.

[0058] In step 1042, the engine adds the production batch number "B0821-L05" to the copper material record, indicating production on production line L05 on August 21st. It also adds the transportation period code "T0803-D07" to the vibration data, indicating transportation to the mining area D07 on August 3rd. Using the production-transportation connection path in the topological network, the engine associates "B0821-L05" with "T0803-D07."

[0059] Step 1043 calculates the material strength index as 99.2% × 0.85 coefficient = 84.32, and the vibration tolerance index as 1.05G × 1.1 coefficient = 1.155. Combining the position offset index with a centrifugal force value of 2.8 for a sharp turn in transportation and the environmental shock index with a temperature rise of 15°C, a complete safety traceability profile is formed: Material Strength: 84.32; Vibration Resistance: 1.155; Position Offset: 2.8; Environmental Shock: 15. Continuing from step 1043: When the material strength index falls below 85 and the vibration tolerance index exceeds 1.1, a "Material Fatigue Risk" alert is triggered.

[0060] The overall solution of step 104 above builds a data association chain for the raw material production and transportation links, realizes cross-node feature fusion through the topological network hierarchical relationship, and forms a multi-dimensional safety assessment model that reflects the coupling effect of material properties and transportation environment, providing a core decision-making basis for the risk management of electronic detonators throughout their life cycle.

[0061] 105. When the abnormal probability value of the detonator state forms an associated abnormal combination with the position offset index and the environmental mutation index in the safety traceability feature, a hierarchical early warning instruction is triggered and the operation authority of the associated node in the supply chain topology network is frozen.

[0062] Optionally, step 105 may specifically include the following steps: 1051. Real-time monitoring of the abnormal probability value of the detonator state, the position offset value in the safety traceability feature, and the amplitude value of the environmental mutation; 1052. When the abnormal probability value of the detonator state continuously exceeds the probability threshold, the position offset value exceeds the displacement allowable value, and the environmental mutation amplitude value exceeds the mutation allowable value, it is determined that an associated abnormal combination is formed; 1053. Send a cargo inspection instruction to the transportation node and a work interruption instruction to the blasting node according to the abnormal indicator type of the associated abnormal combination, and simultaneously block the digital operation authority of the corresponding node in the supply chain topology network.

[0063] In the above scheme, the probability of detonator status abnormality is a dynamic value that quantifies the safety risk of the detonator. The position offset value is the degree to which the transport trajectory deviates from the standard path. The environmental mutation amplitude value is the severity of abnormal changes in temperature and humidity. Digital operation permissions are the data operation permissions granted by supply chain nodes in the management system.

[0064] In this embodiment, a full-link monitoring channel is first established in step 1051. The system then acquires three key parameters in real time through a dedicated data interface: anomaly probability values ​​generated by the collaborative inference engine, updated in milliseconds; position offsets in the security traceability feature, derived from motion trajectory analysis; and environmental mutation amplitudes, derived from temperature and humidity fluctuation monitoring. All parameters are transmitted to a central control platform for dynamic updating.

[0065] Subsequently, step 1052 is used to perform continuous judgment of composite conditions: when the abnormal probability value exceeds the preset probability threshold / 0-1 range for 10 consecutive times, and the position offset exceeds the displacement permission value for 30 seconds, that is, the preset geographic fence range, and the environmental mutation amplitude exceeds the mutation permission value, that is, the preset temperature and humidity safety interval, the system automatically generates an associated abnormal combination identifier.

[0066] Finally, a hierarchical response is initiated through step 1053: the dominant type of abnormal combination is analyzed, i.e., location / environment / composite; an encrypted inspection instruction is sent to the transportation node, including the detonator number and inspection location; an operation interruption instruction is sent to the blasting node, including the risk level and review requirements; and the permissions of associated nodes along the supply chain topology network are frozen to block the data modification function.

[0067] In actual application, through step 1051: a mine blasting project monitors the real-time data stream of the detonator TP-5512: the abnormal probability value: 0.89→0.91→0.93, exceeding the 0.8 threshold for 12 consecutive times; the position offset: 3.2→3.5→3.8, exceeding the 2.0 allowable value for 45 seconds; the environmental mutation amplitude: 12℃→15℃→18℃, exceeding the 10℃ safety range.

[0068] Through step 1052, the system determines that a "triple continuous exceeding of the standard" associated abnormal combination is formed: the probability value continuously exceeds the standard, 0.93>0.8; the offset continuously exceeds the limit, 3.8>2.0; the mutation amplitude breaks the limit, 18℃>10℃.

[0069] Step 1053 identified a "displacement-environmental composite" anomaly. The transport vehicle was instructed to "unpack and inspect container 3-B, detonator TP-5512, in the safe zone." The blasting team was instructed to "suspend loading operations and await confirmation of detonator status." Permissions were frozen: the transport node disabled the "Transport Status Update" function; the production node locked data editing for "Batch B0721"; and the blasting node closed the "Detonator Activation" interface. Upon discovering a detonator seal failure, the system automatically traced the issue back to raw material batch CU-0721, permanently freezing all operational permissions for that batch within the topology network and generating a full-chain risk traceability report.

[0070] The overall solution of step 105 above realizes dynamic collaborative judgment and precise hierarchical control of multi-dimensional risk indicators, blocks the risk transmission path of the supply chain through the authority freezing mechanism, ensures rapid response in all links under abnormal conditions of hazardous materials, and solves the core defect of the disconnection between early warning and execution in traditional solutions.

[0071] The following is a complete embodiment of steps 101 to 105: like Figure 2As shown, when a civil explosives company produces electronic detonators, it constructs a supply chain topology network in its management system through step 101: The raw material supply node records the copper material purity test report for batch CU-0725 from a supplier in City A, with a purity of 99.3%; the production and processing node is associated with detonator production batch B0801, production line L07, and assembly time August 1, 10:00 AM; the storage node is associated with the shelves in Area A of Warehouse No. 3, with a constant temperature of 25°C and a humidity of 55%; the transportation node is assigned to a refrigerated truck with license plate A12345, with a route from City B to the mining area in City C; and the blasting operation node is associated with the iron ore blasting site, with a slope of 38° and a rock hardness of 7.2. Each node forms a bidirectional traceability chain using the digital identity code "B0801-CU0725."

[0072] After step 102, when detonator B0801-CU0725 is loaded into the refrigerated truck, the energy field device on the top of the vehicle emits a 13.56 MHz magnetic field, penetrating the 10 cm pine wood box and activating the sensor unit. The sensor unit records every 5 seconds: location coordinates, 116.4°E→112.5°E, 39.9°N→37.8°N; temperature and humidity inside the box, peak temperature 32°C / humidity 40%; vibration waveform, 0.9 G continuous vibration on mountain roads. The data is transmitted to the local node in the cab via Bluetooth and integrated into a movement trajectory dataset, including 186 coordinate points and an environmental fluctuation dataset, temperature and humidity curves + vibration spectrum.

[0073] Through step 1031, at the edge computing station in the mining area, 200 meters from the blasting site: the collaborative reasoning engine receives the transportation dataset; aligns the timestamps: the location point of 112.35°E, 37.84°N at 14:25 on August 3 is bound to the temperature and humidity value of 31°C / 42% at the same time; inputs the field measured parameters: slope of 38°, magnetite hardness of level 7.5; the engine outputs an abnormal probability value of 0.94, indicating that high temperature transportation and hard rock geology lead to explosive stability risks.

[0074] Through step 104, the system extracts data along the topological network: the raw material node retrieves copper material CU-0725 with a purity of 99.3%; the transportation node obtains a vibration peak of 0.9G; the engine adds the batch number "B0801-L07" to the purity record and the time period code "T0803-14" to the vibration data, August 3, 14:00-15:00; after cross-node matching, a feature subset is generated: material strength index = 99.3 × 0.88 = 87.38; vibration tolerance index = 0.9 × 1.05 = 0.945; the position offset index, sharp turn centrifugal force 3.2, and the environmental mutation index, temperature rise 22°C, are combined to form a four-dimensional safety traceability feature.

[0075] In step 105, real-time monitoring detected a complex anomaly: an anomaly probability of 0.94, persistently exceeding the 0.8 threshold; a position offset of 3.2, exceeding the permitted value by 2.0; and an environmental abrupt change of 22°C, exceeding the permitted value by 15°C. The system implemented hierarchical control measures: The transport vehicle was instructed to "immediately stop at Safety Zone 5 and unpack and inspect detonators in cargo position 7." The blasting team was instructed to "suspend drilling operations pending detonator verification." Permissions were frozen: the transportation node was to disable "in-transit status updates"; the production node was to lock data editing for batch B0801; and the blasting node was to disable the electronic detonator activation function. On-site inspection confirmed the detonator's seal had melted, and the system permanently cancelled the detonator's code and traced it back to raw material batch CU-0725.

[0076] Figure 3 The present application provides a structural diagram of an electronic detonator traceability management and early warning system based on big data, as shown in FIG. Figure 3 As shown, the system includes: Initialization module 31 is used to initialize a supply chain topology network with a hierarchical connection relationship in the electronic detonator production link, wherein the supply chain topology network includes a raw material supply node, a production and processing node, a storage node, a transportation node, and a blasting operation node; An activation module 32 is configured to activate the detonator's built-in sensor unit in a non-contact manner via a pre-installed wireless energy field coupling device when the electronic detonator enters the transportation and storage stages. The wireless energy field coupling device, based on the principle of electromagnetic resonance, penetrates the packaging material and collects the movement trajectory and environmental fluctuation data generated by the activated built-in sensor unit of the detonator; A generation module 33 is configured to deploy a collaborative reasoning engine at an edge computing node near the blasting operation area, perform spatiotemporal correlation between the movement trajectory and the environmental fluctuation data, and combine the correlated data with the topographic and geological parameters of the blasting operation node to generate a detonator state abnormality probability value; An input module 34 is configured to input the raw material supply node quality data and the transportation link vibration intensity data into the collaborative reasoning engine based on the hierarchical connection relationship of the supply chain topology network, perform cross-hierarchical feature fusion, and generate a safety traceability feature containing multi-dimensional risk indicators; The trigger module 35 is used to trigger a hierarchical warning instruction and freeze the operation authority of the associated node in the supply chain topology network when the abnormal probability value of the detonator state forms an associated abnormal combination with the position offset index and the environmental mutation index in the safety traceability feature.

[0077] Figure 3 The electronic detonator traceability management and early warning system based on big data can be executed Figure 1The implementation principles and technical effects of the big data-based electronic detonator traceability management and early warning method described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the big data-based electronic detonator traceability management and early warning system in the above embodiment has been described in detail in the relevant embodiments of the method and will not be elaborated on here.

[0078] In one possible design, Figure 3 The electronic detonator traceability management and early warning system based on big data of the embodiment shown can be implemented as a computing device, such as Figure 4 As shown, the computing device may include a storage component 41 and a processing component 42; The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 42 .

[0079] The processing component 42 is used for the above Figure 1 The embodiment provides a big data-based electronic detonator traceability management and early warning method.

[0080] The processing component 42 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0081] The storage component 41 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0082] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0083] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0084] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0085] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0086] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment provides a big data-based electronic detonator traceability management and early warning method.

[0087] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0088] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0089] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer or server) to execute the methods described in each embodiment or certain portions of the embodiments.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A big data-based electronic detonator traceability management and early warning method, characterized in that: include: Initializing a supply chain topology network with a hierarchical connection relationship in the electronic detonator production link, the supply chain topology network including raw material supply nodes, production and processing nodes, warehousing nodes, transportation nodes and blasting operation nodes; When the electronic detonator enters the transportation and warehousing stage, the detonator's built-in sensor unit is activated in a non-contact state through a pre-installed wireless energy field coupling device. The wireless energy field coupling device penetrates the packaging material based on the principle of electromagnetic resonance and collects the movement trajectory and environmental fluctuation data generated by the activated detonator's built-in sensor unit; Deploy a collaborative reasoning engine at an edge computing node near the blasting operation area to perform spatiotemporal correlation between the movement trajectory and the environmental fluctuation data, and combine the correlated data with the topographic and geological parameters of the blasting operation node to generate a detonator state abnormality probability value; Based on the hierarchical connection relationship of the supply chain topology network, the quality data of the raw material supply nodes and the vibration intensity data of the transportation link are input into the collaborative reasoning engine for cross-hierarchical feature fusion to generate a safety traceability feature containing multi-dimensional risk indicators; When the abnormal probability value of the detonator state forms an associated abnormal combination with the position offset index and the environmental mutation index in the safety traceability feature, a hierarchical early warning instruction is triggered and the operation authority of the associated node in the supply chain topology network is frozen.

2. The method according to claim 1, characterized in that Based on the hierarchical connection relationship of the supply chain topology network, the quality data of the raw material supply nodes and the vibration intensity data of the transportation link are input into the collaborative reasoning engine for cross-hierarchical feature fusion to generate a safety traceability feature containing multi-dimensional risk indicators, including: Obtaining metal purity test records of raw material supply nodes and physical vibration intensity peaks of transportation nodes based on the hierarchical paths of the supply chain topology network; In the collaborative reasoning engine, the metal purity detection records are numbered by production batch, and the physical vibration intensity peak is coded by transportation period, and then cross-node correlation matching is performed; A safety traceability feature subset including material strength index and vibration tolerance index is generated by associating the matching results, and the safety traceability feature subset is merged with the position offset index and the environmental mutation index to form a complete safety traceability feature.

3. The method according to claim 1, characterized in that The collaborative reasoning engine is deployed at the edge computing node near the blasting operation area, the movement trajectory is temporally and spatially correlated with the environmental fluctuation data, and the associated data is combined with the topographic and geological parameters of the operation node of the blasting operation node to generate a detonator state abnormality probability value, including: A collaborative reasoning engine is started at an edge computing node near the blasting operation area to receive the position coordinate sequence from the movement trajectory data and the temperature and humidity change curve from the environmental fluctuation data. Synchronize and align the timestamp of the position coordinate sequence with the acquisition time of the temperature and humidity change curve to form a time-space bound position and environmental status record set; The location and environmental status record set and the terrain slope value and rock hardness value of the blasting operation area are input into the collaborative reasoning engine, and a detonator state abnormality probability value representing the current safety state of the detonator is output.

4. The method according to claim 1, wherein When the electronic detonator enters the transportation and warehousing stage, the preset wireless energy field coupling device activates the detonator's built-in sensor unit in a non-contact state. The wireless energy field coupling device penetrates the packaging material based on the principle of electromagnetic resonance and collects the movement trajectory and environmental fluctuation data generated by the activated detonator's built-in sensor unit, including: Multiple wireless energy field coupling devices are deployed at preset locations on the top of transport carriages and storage shelves. When electronic detonators enter the electromagnetic coverage area, they transmit energy to the detonator's built-in sensor unit through the packaging material through the principle of electromagnetic resonance, activating the detonator's built-in sensor unit to switch from sleep mode to working mode. The activated detonator's built-in sensor unit continuously records the three-dimensional spatial displacement coordinate sequence, the temperature and humidity change curve inside the package, and the physical vibration intensity waveform transmitted from the outside; The three-dimensional spatial displacement coordinate sequence, the temperature and humidity change curve inside the package, and the physical vibration intensity waveform transmitted from the outside are transmitted to local data nodes in the transport compartment and storage shelves through near-field communication, and combined to form movement trajectory data and environmental fluctuation data.

5. The method according to claim 1, wherein When the abnormal probability value of the detonator state forms an abnormal correlation combination with the position offset index and the environmental mutation index in the safety traceability feature, triggering a graded warning instruction and freezing the operation authority of the associated node in the supply chain topology network includes: Real-time monitoring of the abnormal probability value of the detonator state, the position offset value in the safety traceability feature, and the amplitude value of the environmental mutation; When the probability value of the detonator state abnormality continuously exceeds the probability threshold, and the position offset value exceeds the displacement allowable value, and the environmental mutation amplitude value exceeds the mutation allowable value, it is determined that an associated abnormal combination is formed; According to the abnormal indicator type of the associated abnormal combination, a cargo inspection instruction is sent to the transportation node and an operation interruption instruction is sent to the blasting node, and the digital operation authority of the corresponding node in the supply chain topology network is simultaneously blocked.

6. The method according to claim 2, characterized in that The collaborative reasoning engine performs cross-node correlation matching after encoding the metal purity detection records by production batch number and the physical vibration intensity peak by transportation period, including: A unique production batch number is attached to each metal purity test record of the raw material supply node, and the production batch number includes a combination of the supplier code and the production date; Add a transportation period code to the peak value data of physical vibration intensity recorded during the transportation process. The transportation period code consists of the starting loading and unloading point location code and the transportation time interval. In the collaborative reasoning engine, based on the node connection relationship of the supply chain topology network, the metal purity detection records of the same production batch number and the physical vibration intensity peak values ​​coded in the same transportation period are cross-node associated and matched.

7. The method according to claim 3, characterized in that The step of synchronizing the timestamp of the position coordinate sequence with the acquisition time of the temperature and humidity change curve to form a time-space bound position and environmental status record set includes: Assigning a first unified timestamp to each coordinate point in the position coordinate sequence, where the first unified timestamp is derived from a timing clock of the edge computing node; Assigning a second unified timestamp to each monitored value of the temperature and humidity change curve, wherein the second unified timestamp and the first unified timestamp use the same time reference; According to the overlapping interval of the first unified timestamp and the second unified timestamp, the coordinate points of the position coordinate sequence are paired and combined with the monitoring values ​​of the temperature and humidity change curve to form a time-space bound position and environmental status record set.

8. An electronic detonator traceability management and early warning system based on big data, characterized in that: include: An initialization module is used to initialize a supply chain topology network with a hierarchical connection relationship in the electronic detonator production link, wherein the supply chain topology network includes a raw material supply node, a production and processing node, a storage node, a transportation node, and a blasting operation node; An activation module is used to activate the detonator's built-in sensor unit in a non-contact manner when the electronic detonator enters the transportation and warehousing stages through a pre-installed wireless energy field coupling device. The wireless energy field coupling device uses the principle of electromagnetic resonance to penetrate the packaging material and collect the movement trajectory and environmental fluctuation data generated by the activated detonator's built-in sensor unit; A generation module is used to deploy a collaborative reasoning engine at an edge computing node near the blasting operation area, perform spatiotemporal correlation between the movement trajectory and the environmental fluctuation data, and combine the correlated data with the operation node topographic and geological parameters of the blasting operation node to generate a detonator state abnormality probability value; An input module is used to input the quality data of raw material supply nodes and the vibration intensity data of the transportation link into the collaborative reasoning engine based on the hierarchical connection relationship of the supply chain topology network to perform cross-hierarchical feature fusion and generate a safety traceability feature containing multi-dimensional risk indicators; The trigger module is used to trigger a hierarchical early warning instruction and freeze the operation authority of the associated node in the supply chain topology network when the abnormal probability value of the detonator state forms an associated abnormal combination with the position offset index and the environmental mutation index in the safety traceability feature.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an electronic detonator traceability management and early warning method based on big data as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the electronic detonator traceability management and early warning method based on big data as described in any one of claims 1 to 7 is implemented.

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