Internet of Things tracking and state supervision system for civil explosive circulation

Abstract

The invention relates to the technical field of internet-of-things safety supervision, and discloses an internet-of-things tracking and state supervision system for civil explosive circulation, which comprises a circulation working condition sensing module and a cloud platform, and is characterized in that the circulation working condition sensing module is used for acquiring environment parameters, real-time positions and event label information of controlled circulation objects; the cloud platform establishes a digital file including electronic identity codes, controlled job execution codes and entity representation codes which are uniquely associated and mapped, environment parameters are mapped to an industrial mechanism model by utilizing a mechanism state space operator, and stability margin indexes are dynamically corrected by calculating stability potential energy gradients of energetic components. According to the method, deep perception of an energetic material degradation mechanism is realized, supervision logic is subjected to dimension raising from displacement recording to performance prediction, hidden potential safety hazards caused by environmental stress are eliminated, and intrinsic safety of the whole circulation process is guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) security supervision technology, and in particular relates to an IoT tracking and status monitoring system for the circulation of civilian explosive materials. Background Technology

[0002] The current supervision of the circulation of civilian explosives throughout the entire process of production, storage, transportation, and blasting operations falls within the core scope of industrial safety management. Currently, the industry generally adopts a tracking method based on Internet of Things (IoT) technology. By assigning a unique identification code to civilian explosives such as detonators and combining it with scanning records, the circulation path can be traced and the administrative affiliation can be verified. This technology plays a fundamental role in ensuring compliance in the circulation process.

[0003] Existing regulatory methods largely focus on hardware identity verification, while the depth of data interaction and real-time system integration at the software control layer are insufficient. For example, Chinese invention patent CN110445829A discloses an IoT-based information interaction system for the chemical explosives industry. This system, through the construction of underlying hardware acquisition devices and upper-level server architecture, monitors environmental indicators such as energy consumption parameters, harmful gases, temperature, and humidity, and performs ERP cost accounting. While this solution improves meter reading efficiency and environmental awareness, its technical logic essentially involves passively recording external parameters, lacking in-depth quantitative analysis of the intrinsic physical state of energetic materials. In actual circulation, the stability of civilian explosives deteriorates non-linearly due to the cumulative effect of environmental stress. The aforementioned technology only aggregates environmental data and cannot convert temperature, humidity, or vibration data into physical parameters characterizing the internal failure mechanism of the object. This prevents the system from automatically triggering logical circuit breakers when physical performance crosses safety boundaries, making it difficult to eliminate hidden safety hazards caused by environmental stress. In reality, the chemical explosives industry... Explosives are energetic entities with time-varying failure characteristics. The stability of energetic materials is affected by the storage environment. During long-term storage and transportation, the cyclical fluctuations in ambient temperature, humidity penetration, and stress from mechanical vibrations cause irreversible evolution of the physicochemical properties of the agents. Current regulatory methods focus on static recording of external events such as location changes, lacking dynamic perception of the intrinsic state of energetic entities and real-time status visualization based on image data generation technology. When there is a hidden disconnect between the legality displayed by administrative status and the physical stability of the entity, items on the verge of failure may still be authorized to detonate, creating unpredictable safety risks. To address such risks, if stability is determined by increasing the frequency of manual unpacking and random inspections, there is not only the risk of damaging the packaging structure and inducing secondary accidents, but also the detection coverage is limited. If high-precision sensing units are integrated on each item, there are rigid constraints such as engineering costs and energy supply volume in large-scale circulation scenarios.

[0004] Therefore, the technical problem to be solved by this invention is how to accurately characterize the stability evolution of energetic entities during the transfer process and achieve logical circuit breaking without increasing the scale of individual hardware. Summary of the Invention

[0005] This invention provides an Internet of Things (IoT) tracking and status monitoring system for the circulation of civilian explosives, comprising: The flow condition sensing module is used to acquire environmental parameter information, real-time location data and event tag information of the controlled flow object under the flow condition; The cloud platform communicates with the flow condition sensing module to establish digital archives of controlled flow objects. The digital archives include a unique association mapping of electronic identity codes, controlled operation execution codes, and entity representation codes. The cloud platform includes a mechanism state space module, which is used to perform the following steps: Step S1: Obtain environmental parameter information characterizing the physical state of the controlled flow object. The environmental parameter information includes temperature sequence data, humidity sequence data, and random vibration stress data. Step S2: Map environmental parameter information and real-time location data as input variables to the built-in industrial mechanism model, calculate the stability potential energy gradient of the energy-containing components inside the controlled flow object through the mechanism state space operator, and dynamically correct the stability margin index in the digital file based on the stability potential energy gradient. Step S3: If the stability margin index is lower than the preset safety design threshold and it is determined that the physical evolution deterioration trend is met, generate a logical failure instruction for the controlled flow object. The cloud platform also includes an authorization control module, which sends a permission revocation request to the password management node based on the logical failure instruction, so that the controlled job execution code performs a logical circuit breaker operation and is in a failed state.

[0006] Preferably, the cloud platform also includes an event-driven state machine module, which drives the digital archive to automatically transition between different flow nodes based on event tag information; the flow nodes include production nodes, warehousing nodes, transportation nodes, sales nodes, and controlled operation nodes; the event tag information includes barcode scanning events, inbound tags, outbound tags, and operation authorization requests; the event-driven state machine module matches the entity representation code and electronic identity code based on the barcode scanning event to verify identity consistency; provided that the identity consistency is verified and the stability margin index is higher than the preset security design threshold, the event-driven state machine module will transition the digital archive from the current flow state to the next flow state.

[0007] Preferably, the flow condition sensing module includes a temperature and humidity sensor, an accelerometer, and a positioning unit; the flow condition sensing module is used to record the changes in the physical environment under the flow condition according to a preset sampling frequency, and convert the changes in the physical environment into environmental parameter information and send it to the cloud platform; wherein, the real-time location data is obtained by the positioning unit through a satellite positioning protocol.

[0008] Preferably, the cloud platform also includes an identity binding unit, which is used to obtain the electronic identity code generated by the physical identification chip built into the controlled transfer object, the controlled job execution code generated by the cloud platform, and the entity representation code etched on the surface of the controlled transfer object, and to atomically decouple and bind the electronic identity code, the controlled job execution code, and the entity representation code to generate a unique association mapping.

[0009] Preferably, when executing step S2, the mechanism state space module calculates the real-time degradation increment of the stability margin index using the following stability correction formula: Where ΔS is the real-time degradation increment of the stability margin index, α is the temperature response coefficient, and β is the stress accumulation factor. Let be the temperature value at the i-th sampling time, in °C. Let be the random vibration stress value at the i-th sampling time, and Δt be the sampling time interval.

[0010] Preferably, the authorization control module interfaces with the password management node through a dedicated secure link; when the authorization control module receives a job request, it retrieves the stability margin index and circulation status in the digital archive, and sends an authorization message containing the decryption key when the stability margin index is higher than the preset security design threshold and the circulation status is pending job status.

[0011] Preferably, the cloud platform also includes a recycling and cancellation module, which is used to cancel the corresponding electronic identity code after receiving a successful operation feedback message; after receiving an operation failure message or a logic failure instruction, the recycling and cancellation module initiates a forced recycling process for the controlled circulation object.

[0012] Preferably, the mechanism state space module is also used to construct a real-time safety envelope of the controlled flow object based on the operation results of the mechanism state space operator; the real-time safety envelope defines the limit detonation pressure fluctuation range and temperature tolerance upper limit of the controlled flow object under the current environmental stress accumulation.

[0013] Preferably, the cloud platform also includes blockchain evidence storage nodes, which are used to hash and encapsulate the change records of unique association mapping, the correction records of stability margin indicators, and the execution records of logical failure instructions and store them on the blockchain.

[0014] Preferably, the cloud platform is deployed on a cloud server cluster with multi-level security certification, and the flow condition sensing module and the cloud platform use a two-way identity authentication protocol for encrypted data transmission.

[0015] Compared with existing technologies, the IoT tracking and status monitoring system for the circulation of civilian explosives of this invention has the following advantages: 1. In IoT tracking and status monitoring, by deeply binding the physical identification, detonation logic key, and physical carrier code of civilian explosives into a unified whole, a mechanism state model corresponding one-to-one with the physical entity is constructed in the digital space. Image data generation and processing are then performed to generate a digital mirror image reflecting the stability distribution of energetic materials. Using environmental parameters such as temperature and humidity sequences and random vibration stress acquired in real time by the circulation nodes, the stability margin and stress accumulation factor of each explosive are dynamically corrected according to the reaction dynamics mechanism of energetic materials. During the image data generation process, the system uses the corrected stability margin index as a voxel attribute and maps it to the three-dimensional geometric topology space of the controlled circulation object. Grayscale or color image data representing the activity distribution of energetic components is generated through interpolation algorithms and pseudo-color rendering technology. This allows monitoring instructions to be based on calculable image features, enabling the monitoring system to perceive the internal performance degradation of physical entities. This prevents items on the verge of stability failure from being authorized to detonate because their digital files remain in a legal state, and eliminates hidden safety hazards caused by environmental stress.

[0016] 2. By leveraging the synergistic coupling of an event-driven automatic state transition model and a remote authorization cancellation mechanism, a logical circuit breaker system independent of human will is constructed. When the cloud platform determines that the stability margin of an item is lower than the preset safety design threshold, the system automatically triggers the logical failure instruction of the detonation code. This action chain not only completes the state transition in the local state machine, but also simultaneously executes the permission revocation in the password control center. This cross-level linkage mechanism transforms the control of dangerous items from passive recording and attribution to proactive intervention based on physical performance limits, ensuring inherent safety during the circulation process.

[0017] 3. Through the collaborative mechanism of physical coding binding, environmental stress perception, and remote state control, the system achieves dynamic isomorphism between the digital image and the physical entity throughout the entire life cycle of civilian explosive materials. The system no longer relies on external manual scanning instructions to define the state of the materials. Instead, it reconstructs the safety envelope of each material in real time in the digital space by analyzing the accumulated stress in the circulation path. Based on the 3D image reconstruction algorithm, the abstract safety envelope is transformed into visualized 3D state image data. The 3D image reconstruction algorithm extracts the boundary pixel features of the safety envelope and combines the gradient field data output by the industrial mechanism model to perform 3D mesh subdivision and surface fitting in the virtual visual space. This transforms the unstructured environmental stress data into digital image frames with spatial structure information. This deep interweaving of multiple mechanisms not only improves the accuracy of identifying illegally diverted or counterfeit products, but also provides data support based on physical mechanisms for subsequent logistics path optimization and preventive maintenance. This transforms the originally isolated circulation links into a holistic safety assurance system with mutual causal relationships and logical closed loops. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system logic architecture and security monitoring data flow of the present invention; Figure 2 This is a comparison chart of the prediction error evolution trend under different sampling strategies of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0021] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0022] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0023] An IoT tracking and status monitoring system for the circulation of civilian explosives includes a circulation condition sensing module and a cloud platform. The circulation condition sensing module acquires environmental parameter information, real-time location data, and event tag information of the controlled circulation object under circulation conditions. The cloud platform communicates with the circulation condition sensing module to establish a digital file for the controlled circulation object. The digital file contains a unique mapping of electronic identification code, controlled operation execution code, and entity representation code. When the cloud platform executes the initial association procedure for the digital file, the circulation condition sensing module captures 32-bit laser matrix data of the controlled circulation object's shell through a visual recognition unit. The system generates entity representation codes and extracts hardware fingerprints generated by a 128-bit physically unclonable algorithm from the internal storage unit of the controlled flow object as electronic identity codes. The system uses the codes and controlled job execution codes generated by a high-intensity random number generator as input variables, and performs keyed hash operations using the HMAC-SHA256 algorithm to generate unique associated digest values. These digest values ​​are then written as atomic index items into the distributed ledger of the blockchain evidence storage node. This hash anchoring procedure based on physical hardware fingerprints and logical random codes ensures the isomorphism between information flow and material flow during the flow process, eliminating identity verification deviations caused by external tag damage or human tampering.

[0024] The safety supervision of controlled-flow objects involves the performance evolution of energetic materials under flow conditions. Since the stability of energetic materials is affected by the storage environment, environmental parameter information is mapped to a built-in industrial mechanism model through a mechanism state space module. The stability potential energy gradient is defined at the physical level as the rate of change of chemical potential energy of the energetic component within the controlled-flow object as it evolves from a steady state to a thermal runaway critical state. In engineering logic, it is represented as the slope of the stability margin index decaying per unit time with the accumulation of environmental stress. The specific mapping process is as follows: the mechanism state space module pre-stores a dynamic response lookup table based on 1024 sampling points. The system establishes data on the heat release rate of the agent within the temperature range of 30℃ to 80℃. Every minute, environmental parameters are extracted, the difference between the current ambient temperature and the reference temperature of 25℃ is calculated, and this difference is multiplied by a preset attenuation ratio weight of 0.005 to obtain the physical performance loss component within that time step. The industrial mechanism model calculates the stability potential energy gradient of the energetic components inside the controlled flow object using a mechanism state space operator, and dynamically corrects the stability margin index in the digital file based on the stability potential energy gradient. During the mirror entity construction process, the cloud platform establishes a mechanism state vector uniquely corresponding to the physical UID. Its components include a stability margin. Stress accumulation factor and predict failure cycle .

[0025] To detect the degradation mechanism of energetic components, the temperature and humidity sensors and accelerometers in the flow condition sensing module record changes in the physical environment at a preset sampling frequency. The collected environmental parameter information includes temperature sequence data, humidity sequence data, and random vibration stress data. The mechanism state space module executes the following steps: acquiring environmental parameter information characterizing the physical state of the controlled flow object; mapping the environmental parameter information and real-time location data as input variables to the built-in industrial mechanism model; and calculating the real-time degradation increment of the stability margin index using the following stability correction formula: Where ΔS is the real-time degradation increment of the stability margin index, α is the temperature response coefficient, and β is the stress accumulation factor. Here is the temperature value at the i-th sampling time, in °C. Let Δt be the random vibration stress value at the i-th sampling time, and Δt be the sampling time interval. When executing the procedure of mapping environmental parameter information to the industrial mechanism model, the cloud platform calls the pre-calibrated dynamic characteristic matrix and uses the temperature sequence data... With random vibration stress data The collision frequency factor and activation energy bias of the energetic component molecules were converted into a finite element mesh model with 512 nodes to characterize the geometric entity of the controlled flow object. The heat flux distribution of the mesh nodes at ambient temperature was calculated using the heat conduction equation. For random vibration stress, the power spectral density characteristics of the accelerometer output were extracted. The stress concentration factor at the grain boundaries inside the energetic component was calculated using a fatigue damage accumulation algorithm and used as a stability potential energy gradient correction factor to calibrate the response characteristics of the mechanism model. The thermal decomposition exothermic curve of the reagent was collected using a differential scanning calorimeter, and the apparent activation energy of thermal decomposition was extracted. Accelerated aging tests were conducted at a constant temperature of 75℃, recording the time period for the stability index to decrease to 80% of its initial value. The quantified values ​​of the temperature response coefficient α and the stress accumulation factor β were determined to ensure the stability margin index... The calculation process is based on measurable facts of physical and chemical evolution.

[0026] The system executes access control procedures to address potential security risks; when the stability margin is lower than the preset security design threshold... Furthermore, if the determination conforms to the physical evolutionary deterioration trend, the cloud platform generates a logical failure instruction for the controlled flow object; the authorization control module sends a permission revocation request to the password management node based on the logical failure instruction, causing the controlled job execution code to perform a logical circuit breaker operation and enter an invalid state; during this process, even if the flow event tag shows as valid, the system will forcibly trigger the detonation code failure and directly transition the state vector to the pending destruction state; when the authorization control module executes the revocation and circuit breaker procedures of the controlled job execution code, it adopts an active blocking protocol based on a safety state machine model; when the stability margin index determined by the mechanism state space module... Decrease to the safety design threshold At that time, the system sends a logical failure message containing the hash index of the controlled flow object to the password management node; after receiving the message, the password management node forcibly transfers the corresponding controlled job execution code flag from the available state machine to the circuit breaker state machine; when the authorization control module receives the job detonation request, it performs high-speed memory retrieval to verify the real-time status attribute of the flag. If it determines that it is in the circuit breaker state machine, the authorization control module uses internal logic gate circuits to forcibly lock the output port of the detonation control signal and returns a logical lockout response message to the job terminal. This procedure uses the security boundary of physical stability evolution as the hard locking condition of the logic loop to realize the automatic linkage between physical degradation state and logical execution layer permissions.

[0027] The system automates the management of workflow nodes through an event-driven state machine module. These nodes include production, warehousing, transportation, sales, and controlled operation nodes. The event-driven state machine module verifies identity consistency by matching entity representation codes with electronic identity codes based on barcode scanning events. Once identity consistency is verified and the stability margin is higher than a preset security design threshold, the digital file transitions from the current workflow state to the next. The authorization control module interfaces with the password management node via a dedicated secure link. Upon receiving a work request, the system retrieves the stability margin and workflow status from the digital file. Only when the stability margin is higher than the preset security design threshold and the workflow status is "awaiting work" does the system issue an authorization key containing the decryption key. The system employs a logical judgment based on the mapping of physical parameters to safety boundaries, adding a hard safety threshold to the detonation authorization. During system operation, the recycling and cancellation module processes the operation feedback results. Upon receiving a successful operation feedback message, the system cancels the corresponding electronic identity code. Upon receiving an operation failure message or a logical failure instruction, the recycling and cancellation module initiates a mandatory recycling process for the controlled circulation object. In addition, the cloud platform includes blockchain storage nodes, which hash and store the change records of unique association mapping, the correction records of stability margin indicators, and the execution records of logical failure instructions on the blockchain. This multi-dimensional mechanism achieves the isomorphism between the digital mirror and the physical entity of the entire life cycle of civilian explosives, ensuring the inherent safety of the entire circulation process.

[0028] The abstract stability index is converted into visual features using a 3D image reconstruction algorithm. Specific operational procedures include: extracting the 3D geometric topology data of the controlled flow object; and, based on the node numbering of the finite element mesh model, converting the real-time stability margin index of each node. The data is mapped to voxel attribute values ​​at spatial coordinate points; a quantized pseudo-color mapping table is set to linearly map stability indices with values ​​between 20 and 100 to a dark blue to red chromatographic range; a trilinear interpolation algorithm is used to fill the blank areas between grid nodes to generate a three-dimensional digital mirror image characterizing the distribution of the activity of energetic components; to eliminate the spatial information vacuum caused by single sensor data, the pixel calibration procedure for three-dimensional image reconstruction is as follows: the system uses the geometric center of the controlled flowing object as the origin of the coordinate system, mapping 512 finite element grid nodes to a virtual topological space with a length of 100ms, a width of 50ms, and a height of 50ms. Since external sensors can only acquire surface data, the system uses an inverse square distance attenuation algorithm for internal prediction. The thermal response hysteresis constant of the central node is set to 300 seconds. Based on the thermal resistance coefficient of the material along the conduction path, the surface temperature value is assigned to the core nodes in layers with a 2% attenuation every 5ms. During rendering, if the calculated stability index value of a node is below 30, that pixel is marked as high-brightness red in the 3D matrix. Its refresh rate is synchronized with the sampling frequency of the flow condition sensing module, maintaining a frequency of once per minute, detecting the stability margin index. Below the preset safety design threshold At that time, the corresponding failure area in the image is highlighted. By calculating the proportion of the volume of the failure area to the total volume, it is determined that the physical evolution and deterioration trend meets the logical melting condition, providing a quantitative basis with spatial topological characteristics for detonation authorization and forced recovery.

[0029] Example 1: In a long-distance, inter-provincial transportation scenario involving civilian explosives traversing high-temperature areas and complex road conditions, the operating condition sensing module continuously samples the onboard environment, where the ambient temperature fluctuates between 38°C and 52°C. The onboard accelerometer captures random vibration stress values ​​caused by road bumps, with a sampling frequency set to once every 5 minutes. The temperature sequence data and random vibration stress data collected by the operating condition sensing module are uploaded to the cloud platform via an IoT gateway. The mechanism state space module extracts the initial stability margin index from the digital archive, with its initial value set to 95. After the cumulative transportation time reaches 14 hours, the mechanism state space module calculates the real-time degradation increment of the stability margin index using a stability correction formula. Specifically, the stability correction formula is as follows: Where ΔS is the real-time degradation increment of the stability margin index; α is the temperature response coefficient; and β is the stress accumulation factor. Let be the temperature value at the i-th sampling time, in °C; Δt represents the random vibration stress value at the i-th sampling time; Δt is the sampling time interval.

[0030] In this embodiment, the temperature response coefficient α is set to 0.08, the stress accumulation factor β to 0.12, and the sampling time interval Δt to 5 minutes. Due to the superposition of high heat and vibration, the real-time degradation increment ΔS of the stability margin index of the controlled flow object is 77. The system updates the stability margin index. 18 。 Due to the preset safety design threshold The stability margin index is set at 20 by the cloud platform. Less than the preset safety design threshold At this point, the system generates a logical failure command. This command sends a permission revocation request to the password management node through the authorization control module, causing the controlled operation execution code to enter an invalid state at the detonation authorization protocol level. When the item arrives at the work site and the blaster attempts to request authorization through the dedicated equipment, the authorization control module sends back an authorization rejection message because the system has already completed the logical cancellation of the detonation code in the background. At the same time, the recovery and cancellation module initiates the forced recovery process based on the logical failure command. By using the physical degradation mechanism as a hard constraint for permission control, the problem of inconsistency between the legitimacy of external events and internal substantive security is solved.

[0031] Example 2: The purpose of the experiment was to verify the law of multi-physics field coupling stress degradation in the stability of the internal energetic components of the controlled flow object. The experimental platform adopted a high-precision controlled temperature and humidity storage simulation system, in which the thermodynamic environment control accuracy was within the range of 0.1℃ to 0.2℃. The sensing acquisition path integrated a triaxial accelerometer with a measurement resolution of 0.01g. The sampling period setting logic was based on the balance between data acquisition real-time performance and storage load. According to the Nyquist sampling theorem, the sampling time interval Δt was determined to be 1 minute. To simulate a real industrial electromagnetic environment, Gaussian white noise with a signal-to-noise ratio of 15dB and random pulse interference with an amplitude of 5V were superimposed in the signal chain to evaluate the signal extraction stability of the system under non-ideal operating conditions. The experimental group included three sample groups of the present invention with stress gradients, respectively simulating operating conditions with temperature environments of 30℃, 45℃, and 55℃ and superimposed random vibrations with a frequency range of 10Hz to 150Hz. During the experiment, the mechanism state space module acquired the temperature sequence data collected by the flow condition sensing module. With random vibration stress values The evolution trajectory of the stability margin index is calculated based on the stability correction formula; the stability correction formula is as follows: Where ΔS is the real-time degradation increment of the stability margin index; α is the temperature response coefficient; and β is the stress accumulation factor. Let be the temperature value at the i-th sampling time, in °C; Δt represents the random vibration stress value at the i-th sampling time; Δt is the sampling time interval.

[0032] In this embodiment, a temperature response coefficient is set. The stress accumulation factor is 0.08. The value was 0.12. Measurement results showed that under 45℃ conditions, the stability margin index decreased from an initial value of 100 to 82.5 after 24 hours. Under extreme conditions of 55℃ and random vibration, the stability margin index showed a performance inflection point after 18 hours, dropping to 19.2. The slope of this change reflects the accelerating effect of vibrational mechanical energy on the thermal decomposition kinetics of energetic materials. To verify the synergistic effect, a partially missing control group was set up, which removed the stress accumulation factor β component from the stability correction formula. Under the same 55℃ and random vibration conditions, the stability margin index recorded in the control group remained at 42.8 at the end of 24 hours, higher than the preset safety design threshold. The value of 20 indicates that models relying solely on the temperature dimension cannot capture the implicit stability losses caused by vibration stress; and after 18.2 hours of operation, the system determined that the stability margin was below the safety design threshold. This means determining that a physical entity has entered the failure zone and generating a logical failure instruction, which drives the password management node to execute the controlled job execution code for logical circuit breaking.

[0033] Example 3: This example combines Figures 1 to 2 This document describes an IoT-based tracking and status monitoring system for the distribution of civilian explosive materials. Figure 1 As shown, the management node comprises three core components. The flow condition perception module is responsible for collecting environmental parameters, real-time location, and event tag information. It establishes a data connection with the cloud platform through input variable mapping logic. The cloud platform integrates a digital archive, a mechanism state space module, and an authorization control module. The digital archive stores a unique coded association mapping and provides baseline data to the mechanism state space module. The mechanism state space module performs calculations of the stability potential energy gradient and dynamic correction index. After determining the triggering conditions, it generates a logic failure command and sends it to the authorization control module. Based on this, the authorization control module sends a permission revocation request to the password management node, driving the password management node to perform a logic circuit breaker operation and establish a failure state.

[0034] like Figure 2 As shown, the horizontal axis represents time in hours, and the vertical axis represents prediction error in percentage. The chart contains two data curves that extend along the time axis. The solid line corresponds to the prediction error of the adaptive sampling test group, and the dashed line corresponds to the prediction error of the fixed frequency sampling control group. It presents the evolution trend and comparison difference of the prediction error values ​​corresponding to the two different sampling strategies within a time span of 0 to 22 hours.

[0035] Example 4: For controlled transfer objects of energetic materials with fluctuating formulations in different batches, the response characteristics of their internal industrial mechanism model are determined through standardized physical experimental calibration procedures. During the system deployment phase, differential scanning calorimetry is used to collect the thermal decomposition and exothermic curves of the agent at different heating rates, extract the apparent activation energy of the thermal decomposition of the batch of agent, and substitute this physical quantity into the Arrhenius empirical equation. The temperature response coefficient α is determined by establishing a thermodynamic aging model, i.e., accelerated aging experiments are conducted in a closed environment at a constant temperature of 75°C, and the time period required for the stability index to decrease to 80% of the initial value is recorded. Then, the quantitative value of the temperature response coefficient α is obtained by linear regression fitting. The procedure for obtaining the stress accumulation factor β is based on standard random vibration bench experiments. Under the conditions of a frequency range of 20Hz to 2000Hz and an acceleration root mean square value of 5g, the microcrack evolution characteristics of the internal structure of the controlled transfer object are monitored, and the stress accumulation factor β is calibrated by analyzing the conversion efficiency between mechanical energy input and stability potential energy gradient.

[0036] When the flow condition sensing module performs sampling tasks, the cloud platform dynamically adjusts the sampling time interval Δt through a sampling frequency adaptive algorithm; the system calculates the random vibration stress value at the current sampling moment. The rate of change relative to the previous moment, i.e., the rate of change of stress. To characterize the intensity of changes in environmental disturbances; stress variability. The calculation formula is as follows: ,in, For stress variation; This represents the random vibration stress value at the current sampling time; This represents the random vibration stress value at the previous sampling time. For the current sampling time interval; when the stress variation... When the rate of change exceeds the preset threshold of 5, the operating condition is determined to have entered the unsteady state range. At this time, the mechanism state space module generates a frequency switching command, which drives the sensing unit to shorten the sampling time interval Δt from 5 minutes to 1 minute, thereby improving the resolution of capturing instantaneous jumps in the stability potential energy gradient.

[0037] The system calibrates the safety design threshold based on the thermal runaway critical capacity of energetic materials. Experiments showed that when the stability margin of the controlled-flow object decreased to 15% of its initial value, the heat release rate of its self-accelerating decomposition reaction would exceed the heat dissipation rate of the environment. This point was determined to be the physical failure critical point. To provide a safety margin, the system set a safety design threshold. Set to 20; when the stability margin index output by the mechanism state space module is... When the value drops below 20, the authorized control module triggers the logic circuit breaker of the controlled operation execution code. By executing the above parameter calibration procedure and adaptive sampling process, the system achieves isomorphism between physical intrinsic characteristics and digital mirror calculation logic. Experimental data confirms that under transportation conditions with drastic fluctuations in environmental stress, the prediction error of the stability index of the test group using adaptive sampling is no higher than 2.5%, while the prediction error of the control group using fixed frequency sampling is 14.2% under the same conditions. This eliminates the arbitrariness of model parameter selection and ensures the inherent safety of the entire flow process.

[0038] Example 5: When executing the warehousing parameter extraction procedure after changes in the energetic material component formulation, the system extracts the exothermic peak temperature of the reagent inside the controlled transfer object. The intrinsic thermal decomposition apparent activation energy of this batch was determined using differential scanning calorimetry data. and the apparent activation energy Input the mechanism state space module of the cloud platform; record the time period required for the stability index to decrease to 80% of the initial value through accelerated aging tests in a closed environment at a constant temperature of 75℃; use the least squares method to fit and determine the temperature response coefficient α and the initial stability margin index corresponding to this batch of controlled flow objects; the system writes the above parameters into a digital file associated with the physical UID as a mechanism state vector. The initial boundary of evolution.

[0039] Under deployment conditions where there is environmental background noise interference in the sensing acquisition link, the sensing unit continuously collects 60 sets of environmental data in an unloaded state; the system calculates the random vibration stress value. The arithmetic mean of the values ​​is determined as the characteristic value of the environmental background stress. The mechanism state space module will use eigenvalues As a compensation operator injected into the stability correction formula, the deviation component induced by background stress is deducted in the real-time degradation increment ΔS calculation process of the stability margin index; the cloud platform verifies the calibration data record in the blockchain storage node, binds the physical UID with the electronic identity code for initial execution, and makes the detonation authorization logic of the controlled operation execution code act within the physical performance envelope corrected by the background signal.

[0040] Example 6: During the engineering deployment phase of connecting the controlled flow object to the flow condition sensing module, technicians used a high-precision standard vibration table to execute the sensing gain coefficient. With zero offset Calibration procedure, sensor gain coefficient The calculation formula is as follows: ,in, The sensing gain coefficient is... This represents the output voltage value of the sensing unit under full-scale operating conditions. This represents the output voltage value of the sensing unit under zero-point operating conditions. Using the standard reference acceleration value, the mechanism state space module obtains the calibrated sensing gain coefficient. The system normalizes the original voltage signal to extract physical tensor data. Based on the energetic materials of different production batches within the controlled flow object, the system establishes a finite element mesh model with 512 nodes in the digital space, calculates the rate of change of heat flux of the energetic component per unit time and corrects the stability potential gradient. The data conversion procedure eliminates the mechanism mapping deviation induced by individual differences in sensing hardware.

[0041] When the cloud platform executes remote detonation authorization decisions via the IoT gateway, the system runs a prediction compensation procedure based on the least squares method to offset network transmission latency. The impact on the isomorphism between digital mirrors and physical entities; the cloud platform extracts the environmental parameter sequence containing timestamps sent by the flow condition sensing module, and establishes a sliding time window with a length of 10 sampling periods in the cloud; the authorized control module uses the stability margin index to predict values. Instead of using the original received value as the criterion for logic circuit breaking, the predicted value of the stability margin index is used. The linear constraint relationship is as follows: ,in, This is the predicted value of the stability margin index. This represents the current received stability margin value. The rate of change of the stability margin index over time. To measure network transmission latency, the system determines that when a physical entity experiences a transmission delay of 250ms in a standard warehousing environment at 15℃, it compensates for the incremental performance degradation during this period through a predictive compensation procedure, so that the triggering time of the logic circuit breaker instruction and the time when the energetic material inside the physical entity crosses the safety boundary are aligned within a 50ms error range.

[0042] The recovery and accounting module executes a successful operation confirmation procedure based on acoustic and vibration signal feature extraction during the closed-loop management of post-blasting operations. The system pre-deploys acoustic sensing units with a sampling frequency of no less than 20kHz at controlled operation nodes to capture pressure waveform data generated by the blasting operation in real time. The mechanism state space module uses a fast Fourier transform algorithm to analyze the spectral energy density distribution of the acquired acoustic signals and calculates the peak energy within the main frequency bandwidth. And compare it with the preset successful detonation energy characteristic matrix; if determined Above the feature threshold If the time-axis evolution of the energy envelope conforms to the dynamics of detonation reaction, the system will automatically send a cancellation instruction to the cloud platform to cancel the corresponding electronic identity code; if the acoustic vibration characteristic energy is determined to be lower than the preset threshold or the signal envelope is missing, the system will determine that the controlled transfer object is in an incomplete reaction state or a dud state, and generate a forced recycling instruction for the electronic identity code.

[0043] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. An Internet of Things (IoT) tracking and status monitoring system for the circulation of civilian explosives, characterized in that, include: The flow condition sensing module is used to acquire environmental parameter information, real-time location data and event tag information of the controlled flow object under the flow condition; The cloud platform communicates with the flow condition sensing module to establish digital archives of controlled flow objects. The digital archives include a unique association mapping of electronic identity codes, controlled operation execution codes, and entity representation codes. The cloud platform includes a mechanism state space module, which is used to perform the following steps: Step S1: Obtain environmental parameter information characterizing the physical state of the controlled flow object. The environmental parameter information includes temperature sequence data, humidity sequence data, and random vibration stress data. Step S2: Map environmental parameter information and real-time location data as input variables to the built-in industrial mechanism model, calculate the stability potential energy gradient of the energy-containing components inside the controlled flow object through the mechanism state space operator, and dynamically correct the stability margin index in the digital file based on the stability potential energy gradient. Step S3: If the stability margin index is lower than the preset safety design threshold and it is determined that the physical evolution deterioration trend is met, generate a logical failure instruction for the controlled flow object. The cloud platform also includes an authorization control module, which sends a permission revocation request to the password management node based on the logical failure instruction, so that the controlled job execution code performs a logical circuit breaker operation and is in a failed state.

2. The Internet of Things tracking and status monitoring system for the circulation of civilian explosives according to claim 1, characterized in that, The cloud platform also includes an event-driven state machine module, which is used to drive the digital archive to automatically transition between different flow nodes based on event tag information; the flow nodes include production nodes, warehousing nodes, transportation nodes, sales nodes, and controlled operation nodes; Event tag information includes barcode scanning events, inbound tags, outbound tags, and job authorization requests; The event-driven state machine module verifies identity consistency by matching the entity representation code and electronic identity code based on the scanning event. If the identity consistency is verified and the stability margin index is higher than the preset security design threshold, the event-driven state machine module will transition the digital file from the current circulation state to the next circulation state.

3. The Internet of Things tracking and status monitoring system for the circulation of civilian explosives according to claim 2, characterized in that, The flow condition sensing module includes a temperature and humidity sensor, an accelerometer, and a positioning unit. The flow condition sensing module is used to record changes in the physical environment under the flow condition according to a preset sampling frequency, and convert the physical environment changes into environmental parameter information and send it to the cloud platform. The real-time location data is obtained by the positioning unit through a satellite positioning protocol.

4. The Internet of Things tracking and status monitoring system for the circulation of civilian explosives according to claim 1, characterized in that, The cloud platform also includes an identity binding unit, which is used to obtain the electronic identity code generated by the physical identification chip built into the controlled transfer object, the controlled job execution code generated by the cloud platform, and the entity representation code etched on the surface of the controlled transfer object, and to atomically decouple and bind the electronic identity code, the controlled job execution code, and the entity representation code to generate a unique association mapping.

5. The Internet of Things (IoT) tracking and status monitoring system for the circulation of civilian explosives according to claim 1, characterized in that, When executing step S2, the mechanism state space module calculates the real-time degradation increment of the stability margin index using the following stability correction formula: Where ΔS is the real-time degradation increment of the stability margin index, α is the temperature response coefficient, and β is the stress accumulation factor. Let be the temperature value at the i-th sampling time, in °C. Let be the random vibration stress value at the i-th sampling time, and Δt be the sampling time interval.

6. The Internet of Things tracking and status monitoring system for the circulation of civilian explosives according to claim 1, characterized in that, The authorization control module connects to the password management node through a dedicated secure link. When it receives a job request, the authorization control module retrieves the stability margin index and circulation status in the digital archive. When the stability margin index is higher than the preset security design threshold and the circulation status is pending job status, the authorization message containing the decryption key is sent out.

7. The Internet of Things (IoT) tracking and status monitoring system for the circulation of civilian explosives according to claim 1, characterized in that, The cloud platform also includes a recycling and cancellation module, which is used to cancel the corresponding electronic identity code after receiving a successful operation feedback message; after receiving an operation failure message or a logic failure instruction, the recycling and cancellation module initiates a forced recycling process for the controlled circulation object.

8. The Internet of Things tracking and status monitoring system for the circulation of civilian explosives according to claim 1, characterized in that, The mechanism state space module is also used to construct the real-time safety envelope of the controlled flow object based on the operation results of the mechanism state space operator; the real-time safety envelope defines the limit detonation pressure fluctuation range and temperature tolerance upper limit of the controlled flow object under the current environmental stress accumulation.

9. The Internet of Things (IoT) tracking and status monitoring system for the circulation of civilian explosives according to claim 1, characterized in that, The cloud platform also includes blockchain evidence storage nodes, which are used to hash and store on the blockchain the change records of unique association mapping, the correction records of stability margin indicators, and the execution records of logical failure instructions.

10. The Internet of Things tracking and status monitoring system for the circulation of civilian explosives according to claim 1, characterized in that, The cloud platform is deployed on a cloud server cluster with multi-level security certification. The flow condition sensing module and the cloud platform use a two-way identity authentication protocol for encrypted data transmission.

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