Mining wireless transmission iris recognition three-person interlocking detonation controller

Abstract

The invention relates to the technical field of mining safety equipment, and discloses a mining wireless transmission iris recognition three-person interlocking detonation controller which comprises an iris acquisition module, an identity verification module, an interlocking judgment module, a mode matching module, a reference calibration module and an execution control module. The iris acquisition module acquires an iris image signal of an operator and divides an identity verification interval; the identity verification module extracts iris feature nodes to generate feature vectors; the interlocking judgment module establishes an interlocking verification rule; the mode matching module calculates an identity verification deviation; the reference calibration module deduces an optimal verification threshold value; and the execution control module generates a detonation control scheme. Three-person interlocking verification is achieved through the wireless transmission and iris recognition technology, and the safety and operation efficiency of mine blasting operation are improved.

Description

Technical Field

[0001] The invention relates to the technical field of mine safety equipment, in particular to a mine-used wireless transmission iris recognition three-person interlocking detonation controller. Background Art

[0002] In mining blasting operations, the safety and reliability of detonation control have always been a key focus of the industry. Traditional detonation control systems have significant deficiencies in identity authentication and permission management. On the one hand, traditional identity authentication methods based on passwords and cards are prone to fraudulent use and theft, making it impossible to accurately verify the true identity of operators, posing a safety hazard. On the other hand, multi-person interlocking control mechanisms are often cumbersome and inconvenient in actual application, making it difficult to effectively achieve permission coordination and mutual supervision among multiple personnel. This, to a certain extent, affects the efficiency and safety of blasting operations.

[0003] The development of intelligent mining has placed higher demands on the intelligence and security of detonation controllers. While some existing biometric recognition technologies have been introduced for detonation control, most rely on a single biometric feature, such as fingerprint recognition. Their accuracy and stability are susceptible to environmental factors, and in harsh, humid, and dusty environments like those found in mines, the recognition success rate drops significantly. Furthermore, traditional wired transmission methods are difficult to route in complex mine environments, resulting in high maintenance costs and susceptibility to physical damage, which can impact system operation.

[0004] Furthermore, the existing three-person interlock detonation system lacks flexibility and adaptability in authorization verification and control logic. Different blasting scenarios and phases require different authorization verification strategies. Traditional systems struggle to dynamically adjust verification strategies and thresholds based on actual conditions, potentially leading to misjudgments or missed detections in some specific situations and failing to fully ensure the safe conduct of blasting operations. Furthermore, the collaboration between modules is inefficient, and data processing and transmission speeds are slow, failing to meet the real-time requirements of mine blasting operations.

[0005] In terms of wireless transmission, existing mine-used wireless transmission technology lacks signal stability and anti-interference capabilities. It is susceptible to interference from the complex electromagnetic environment within mines, leading to data interruptions or errors, affecting the accuracy and timeliness of detonation control. Furthermore, wireless transmission security is a pressing issue. Preventing data theft or tampering during transmission and ensuring the security and reliability of detonation control commands are key challenges facing current mine-used wireless transmission detonation systems. Summary of the Invention

[0006] The purpose of the present invention is to provide a mine-used wireless transmission iris recognition three-person interlocking detonation controller to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a mine-used wireless transmission iris recognition three-person interlock detonation controller, the controller comprising: The iris acquisition module is used to obtain the iris image signal of the operator and set the identity verification interval corresponding to the three-person interlocking scenario; The identity verification module is used to divide the identity verification interval into multiple iris feature nodes, extract features from the iris data of each node, and generate an iris feature vector corresponding to the node; The interlock determination module is used to extract the permission association index from the iris feature vector, establish the interlock verification rule associated with the iris feature node, and obtain the permission confirmation parameter corresponding to the rule; The pattern matching module is used to identify the detonation permission pattern in the permission confirmation parameters, adaptively match the permission association indicators according to the permission pattern, and calculate the identity verification deviation of each node under different matching strategies; The benchmark calibration module is used to derive the optimal verification threshold based on the identity verification deviation and generate the permission deviation sequence by comparing the current iris feature value with the optimal verification threshold; The execution control module is used to parse the permission deviation sequence and integrate the permission deviation sequence into a mine-used wireless transmission iris recognition three-person interlocking detonation control solution based on the node's identity verification direction.

[0008] Preferably, the implementation of the identity verification module includes: constructing a biometric database corresponding to the iris feature node, the biometric database containing the iris image signal and the feature parameter vector of the iris data mapping; Perform feature point matching on the feature parameter vector, and divide the iris verification interval of the feature parameter vector according to the matching result; extract the feature critical point of the iris data from the iris verification interval, and set the feature critical point as the iris feature node.

[0009] Preferably, the iris verification interval for dividing the feature parameter vector further includes: Extract image clarity, feature matching rate, and biometric parameters based on the iris type and operation stage in the feature parameter vector, and generate a biometric tag based on the above parameters; The biometric tags are associated with the feature parameter vectors, and the iris equivalence between the tags is calculated. The feature parameter vectors with equivalence higher than the preset iris threshold are selected to form the iris verification interval.

[0010] Preferably, the implementation of generating the iris feature vector corresponding to the node includes: For each iris feature node, obtain the node's feature matching data within a preset period based on the node's temporal position in the identity verification interval, and calculate the node's feature matching coefficient; When the feature matching coefficient exceeds the first verification threshold, the node is marked as a high-frequency verification node, and its iris data is extracted to form an iris feature vector; when the feature matching coefficient is lower than the first verification threshold, the node is marked as a steady-state node, and the iris data of the node's adjacent nodes are feature superimposed, and the superimposed data is reconstructed into an iris feature vector.

[0011] Preferably, the implementation of the interlocking determination module includes: Separate the main operation authority, the secondary operation authority and the feature deviation parameters from the iris feature vector, and generate the interlocking verification rules of the iris feature node based on the above parameters; If the number of verification nodes covered by the current interlocking verification rule is less than the preset verification threshold, the iris feature vectors of adjacent iris feature nodes are traversed, and the permission indicators not included in the verification rules of the adjacent nodes are added to the current rule.

[0012] Preferably, the implementation of the pattern matching module includes: obtaining a characteristic factor of a matching frequency and a matching factor of an authority response rate in a detonation permission pattern; A pattern transition network associated with characteristic factors and matching factors is constructed, and the identity verification bias under different matching strategies is determined based on the transition probability of each path in the network.

[0013] Preferably, constructing the mode transfer network further includes: Identify the periodic characteristics of the characteristic factor. If the current periodic characteristics completely match the preset operation period, then set the characteristic factor as the starting node of the mode transfer network. Calculate the matching correlation between the characteristic factor and the matching factor, and generate the intermediate nodes and terminal nodes of the pattern transfer network in descending order of correlation; The terminal node is backtracked, and when the correlation degree of the terminal node is lower than the preset matching threshold, it is output as the final path of the pattern transfer network.

[0014] Preferably, the implementation of calculating the identity verification deviation includes: Statistically calculate the mean value of the characteristic factor and the range of the matching factor of each terminal node in the pattern transfer network, and calculate the global covariance of all node factors; The characteristic fluctuation coefficient is obtained by subtracting the mean characteristic factor of a single terminal node from the mean characteristic factor of the adjacent nodes and dividing it by the global covariance. At the same time, the ratio of the matching factor range to the global covariance is calculated, and the weighted sum of the two is used as the identity verification deviation of the node.

[0015] Preferably, the implementation of deriving the optimal verification threshold includes: Extract the verification mode from the historical data that is closest to the current identity verification deviation, and calculate the Euclidean distance between the two in the feature distribution as the first calibration reference value; Count the difference in the number of feature points between the current identity verification deviation and the historical verification mode, and use the difference as the second calibration reference value; Based on the linear combination of the first calibration reference value and the second calibration reference value, an optimal verification threshold in a preset verification threshold table is matched.

[0016] Preferably, the implementation of the execution control module includes: dividing the positive permission interval and the negative permission interval according to the identity verification direction of each node in the permission deviation sequence; The attenuation rate of the authority deviation in the positive authority interval and the increase rate of the authority deviation in the negative authority interval are extracted, and the two are weightedly fused according to the operation weight of the iris feature node to generate the authority parameters of the mine-used wireless transmission iris recognition three-person interlocking detonation control scheme.

[0017] Compared with the prior art, the present invention has the following beneficial effects: In the identity recognition process, the iris acquisition module is used to obtain the iris image signal of the operator and set the identity authentication interval. Combined with the identity verification module, the iris feature nodes are divided within the interval and the feature vector is extracted. The uniqueness and stability of the iris as a biometric feature are utilized to greatly improve the accuracy and reliability of identity authentication, effectively avoiding the possible impersonation and theft that may occur in traditional authentication methods, and ensuring the authenticity of the identity of personnel in blasting operations from the source.

[0018] In terms of authority management and interlocking control, the interlocking determination module extracts authority-related indicators from iris feature vectors and establishes interlocking verification rules. The pattern matching module identifies the detonation permission pattern and adaptively matches the authority-related indicators, calculating identity verification deviations, enabling the system to dynamically adjust verification strategies based on different operational scenarios and phases. This flexible authority management mechanism achieves precise coordination of three-person interlocking control, not only meeting the safety regulations' requirements for multi-person supervision, but also improving the efficiency and rationality of authority verification through scientific rule establishment and pattern matching, avoiding the cumbersome and inefficient processes of traditional interlocking mechanisms.

[0019] In terms of data processing and verification threshold optimization, the benchmark calibration module derives the optimal verification threshold based on identity verification deviations and generates a permission deviation sequence. This allows the system to dynamically optimize verification standards based on historical data and current deviations, improving the system's adaptability to diverse environments and operating conditions. Compared to traditional systems with fixed thresholds, this dynamic calibration mechanism significantly reduces the probability of misjudgments and missed detections, further enhancing the safety of detonation control.

[0020] In terms of system integration and control execution, the execution control module analyzes the permission deviation sequence and integrates it into a control solution. Combined with wireless transmission technology, this eliminates the limitations of traditional wired transmission, which often require difficult wiring and high maintenance costs, and improves the system's applicability and flexibility in complex mining environments. Furthermore, wireless transmission, coupled with the coordinated operation of the system's various modules, accelerates data processing and transmission, meeting the real-time requirements of blasting operations.

[0021] Furthermore, in feature matching and network construction, the system deeply analyzes the relationship between feature factors and matching factors by constructing a pattern transfer network and calculating feature fluctuation coefficients. This improves the system's ability to process feature data and resist interference in complex environments. The establishment of a biometric database and the matching of feature parameter vectors further ensure the stability and reliability of iris recognition, maintaining a high recognition success rate even in harsh environments such as humid and dusty mines. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a working principle diagram of the mine-used wireless transmission iris recognition three-person interlocking detonation controller according to the present invention; Figure 2 Schematic diagram of the identity verification module; Figure 3 Schematic diagram for iris feature vector generation; Figure 4 Schematic diagram for the derivation of the optimal validation threshold. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] See also Figure 1-Figure 4 The present invention relates to a mine-used wireless transmission iris recognition three-person interlock detonation controller, which includes an iris acquisition module for acquiring iris image signals of operators and setting identity verification intervals corresponding to the three-person interlock scenario. Specific implementation methods are as follows: The identity verification module divides the identity authentication interval into multiple iris feature nodes, extracts features from the iris data of each node, and generates an iris feature vector corresponding to the node. The interlocking determination module extracts the authority association index from the iris feature vector, establishes the interlocking verification rules associated with the iris feature nodes, and obtains the authority confirmation parameters corresponding to the rules. The pattern matching module identifies the detonation permission mode in the authority confirmation parameters, adaptively matches the authority association index according to the permission mode, and calculates the identity verification deviation of each node under different matching strategies. The benchmark calibration module derives the optimal verification threshold based on the identity verification deviation, and generates a permission deviation sequence by comparing the current iris feature value with the optimal verification threshold. The execution control module parses the permission deviation sequence and integrates the permission deviation sequence into a three-person interlocking detonation control scheme for mines based on the identity verification direction of the node.

[0025] Example 1:

[0026] This embodiment involves a specific implementation of an identity verification module. This module plays a key role in extracting and verifying the iris features of operators in a mine-used wireless transmission iris recognition three-person interlock detonation controller. The specific implementation process is as follows: The identity verification module requires the construction of a biometric database corresponding to iris feature nodes. This database provides the fundamental data support for the entire identity verification process, containing a large number of iris image signals and feature parameter vectors mapped from this iris data. These iris image signals are acquired by the iris acquisition module during the initial worker registration phase and contain key information such as iris texture and color for each worker. The feature parameter vectors are digital representations of the iris image signals obtained after preliminary processing, facilitating subsequent feature matching and analysis.

[0027] After building a biometric database, feature point matching is required for the feature parameter vectors. Feature point matching is a core step in identity verification. Using a specific algorithm, the feature parameter vectors in the biometric database are compared one by one to identify representative feature points. These feature points are typically unique and stable locations in the iris texture, such as iris edges, wrinkles, and pigmented spots. Accurately identifying these feature points provides a reliable basis for subsequent iris verification interval division.

[0028] Based on the feature point matching results, the identity verification module divides the feature parameter vector into iris verification intervals. This division requires comprehensive consideration of multiple factors, including the iris type in the feature parameter vector and the operation phase. Different iris types may have different texture characteristics and complexity, and the identity verification requirements and environmental conditions may vary across different operation phases. For example, the accuracy and speed requirements for identity verification may differ during the preparation and execution phases of a blasting operation.

[0029] Taking into account the iris type and the operation stage, the identity verification module extracts key metrics such as image clarity, feature matching rate, and biometric parameters from the feature parameter vector. Image clarity reflects the quality of the iris image and directly affects the accuracy of subsequent feature extraction; the feature matching rate indicates the degree of match between the iris features being verified and those in the biometric database; and the biometric parameters include various physiological characteristics of the iris. Based on these extracted parameters, the corresponding biometric tag is generated.

[0030] Biometric tags are associated with feature parameter vectors. By establishing a correspondence between tags and vectors, feature parameter vectors can be more easily classified and managed. Then, by calculating the iris equivalence between tags, feature parameter vectors with equivalence above a preset iris threshold are selected. These selected feature parameter vectors together constitute the iris verification interval. The preset iris threshold is pre-set based on actual operational requirements and security standards. Only when the iris equivalence between tags reaches or exceeds this threshold will the corresponding feature parameter vector be included in the iris verification interval, ensuring the accuracy and reliability of subsequent identity verification.

[0031] After the iris verification interval is divided, the characteristic critical points of the iris data are extracted from this interval and set as iris feature nodes. Feature critical points are locations in the iris data that show significant changes or key features, effectively characterizing the uniqueness of the iris. By using these characteristic critical points as iris feature nodes, iris features can be described and analyzed more accurately, laying the foundation for subsequent generation of iris feature vectors and identity verification.

[0032] Example 2:

[0033] This embodiment involves a specific implementation method for generating an iris feature vector corresponding to a node. This process is a key step in implementing identity verification and authority control in a mine-used wireless transmission iris recognition three-person interlock detonation controller. The specific implementation steps are as follows: For each iris feature node, relevant data is acquired based on its temporal position within the identity verification interval. The identity verification interval is pre-set by the iris acquisition module based on a three-person interlocking scenario and contains multiple iris feature nodes arranged in chronological order. Each node has a specific temporal position within the identity verification interval, which reflects its order and timing within the entire identity verification process.

[0034] Based on the node's temporal position, the system acquires feature matching data for that node within a preset period. This period is a pre-determined timeframe based on the actual needs of mining operations and the technical characteristics of iris recognition, ranging from milliseconds to seconds. During this period, the system continuously collects and records feature matching data related to the iris feature node. This data includes information such as the match status of the node's iris features with features in the biometric database, the number of matches, and the strength of the match.

[0035] After acquiring feature matching data within a preset period, the feature matching coefficient for that node needs to be calculated. This coefficient is an important indicator used to measure the degree of iris feature node matching. It is calculated through comprehensive analysis and calculation of the feature matching data. The specific calculation method may involve weighting parameters such as the number of matches and matching strength to comprehensively reflect the feature matching status of the node.

[0036] The iris feature nodes are classified based on the comparison result between the calculated feature matching coefficient and the first verification threshold value, which is a critical value pre-set according to the safety requirements of mining operations and the performance indicators of the iris recognition system.

[0037] When the feature matching coefficient exceeds the first verification threshold, the iris feature node has a high degree of match, indicating high reliability and importance. At this point, the node is marked as a high-frequency verification node, and its iris data is extracted to form an iris feature vector. High-frequency verification nodes require more frequent verification during the identity verification process to ensure the accuracy of their identities. The extracted iris data contains all the key feature information of the node. By organizing and arranging this data, an iris feature vector is formed, which accurately represents the iris characteristics of the high-frequency verification node.

[0038] If the feature matching coefficient falls below the first verification threshold, the matching degree for the iris feature node is low, indicating possible uncertainty or interference. In this case, the node is marked as a stable node. For stable nodes, iris feature analysis alone may not yield accurate results, so feature superposition processing is required on the iris data of adjacent nodes.

[0039] Adjacent nodes refer to the nodes that are temporally adjacent to the steady-state node during the authentication interval. By collecting and analyzing the iris data of these adjacent nodes and overlaying their features, the feature information of these adjacent nodes can be comprehensively utilized to compensate for the deficiencies of the steady-state node's own features. This feature overlay process may involve weighted fusion and feature merging of the iris data of adjacent nodes to generate more comprehensive and reliable feature data.

[0040] After superimposing the iris data of adjacent nodes, the superimposed data needs to be reconstructed to form an iris feature vector. This reconstruction process organizes and processes the superimposed feature data according to specific rules and algorithms, creating a vector that effectively represents the combined characteristics of the steady-state node and its adjacent nodes. This generated iris feature vector can, to a certain extent, overcome the problem of insufficient feature matching within the steady-state nodes themselves, improving the accuracy and reliability of identity verification.

[0041] Throughout the iris feature vector generation process, the accurate determination of timing positions, the appropriate setting of preset periods, the scientific calculation of feature matching coefficients, and the classification and processing of different node types and the corresponding feature vector generation methods all directly impact the quality of the iris feature vector and the effectiveness of identity verification. By performing targeted processing on each iris feature node, whether it is a high-frequency verification node or a steady-state node, accurate and reliable iris feature vectors can be generated, providing effective data support for subsequent modules such as interlock determination and pattern matching.

[0042] This method of generating iris feature vectors fully accounts for various interference factors that may exist in mining environments, as well as fluctuations in feature matching that may occur during iris recognition. By implementing different processing strategies for nodes with varying degrees of matching, the system's adaptability and reliability are effectively improved. This ensures that, in a three-person interlock detonation scenario, only operators with accurate identity verification are granted the appropriate operational permissions, thus ensuring safe mining operations.

[0043] Example 3:

[0044] This embodiment involves a specific implementation of an interlock determination module. This module, in a mine-used wireless transmission iris recognition three-person interlock detonation controller, undertakes the key functions of extracting authority-related indicators from iris feature vectors, establishing interlock verification rules, and obtaining authority confirmation parameters. The specific implementation process is as follows: The interlock determination module needs to separate primary and secondary operation permissions, as well as feature deviation parameters, from the iris feature vector. The iris feature vector is generated by the identity verification module and contains detailed feature information for each iris feature node. Primary operation permissions correspond to the operator's primary operational responsibilities in a three-person interlock detonation scenario, while secondary operation permissions correspond to those for auxiliary operations. Feature deviation parameters reflect the degree of difference between the current iris features and the standard features in the biometric database. Using specific algorithms and rules, the interlock feature vector is parsed and processed, separating these different types of permission and deviation parameters for subsequent targeted analysis and processing.

[0045] After separating the main operating authority, deputy operating authority and characteristic deviation parameters, the interlocking verification rules of the iris feature nodes are generated based on these parameters. The interlocking verification rules are the core criteria to ensure the effective implementation of the three-person interlocking detonation mechanism. They stipulate the verification conditions and processes that operators with different permissions need to meet in different scenarios. For example, it may be stipulated that the main operating authority personnel must complete the verification of all key nodes, and the deputy operating authority personnel need to perform auxiliary verification at specific nodes, and at the same time combine the characteristic deviation parameters to determine whether the verification is passed. When generating interlocking verification rules, it is necessary to fully consider the safety requirements of mining operations, the logical relationship of three-person interlocking, and the technical characteristics of iris recognition to ensure the rationality and effectiveness of the rules.

[0046] After generating interlocking verification rules, the number of verification nodes covered by the current rules needs to be checked. The preset verification threshold is a critical number of nodes set based on mining safety standards and the actual requirements of three-person interlocking. It represents the minimum number of verification nodes required to ensure the safety of blasting operations. If the number of verification nodes covered by the current interlocking verification rules is less than the preset verification threshold, it indicates that the current verification rules may not fully guarantee the safety of blasting operations and need to be supplemented and improved.

[0047] When supplementing or improving a rule, the interlocking judgment module traverses the iris feature vectors of adjacent iris feature nodes. Adjacent iris feature nodes are the nodes that are temporally adjacent to the node involved in the current rule within the authentication interval. By traversing the iris feature vectors of these adjacent nodes, the module retrieves the permission indicator information contained therein. These permission indicators may not be included in the current rule, such as auxiliary permissions for specific operation stages or special permissions for emergency situations.

[0048] After obtaining the permission indicators of adjacent nodes, these permission indicators that are not included in the current rules are added to the current rules. In this way, the interlocking verification rules are gradually expanded and improved to cover more verification nodes and permission indicators, ensuring that the rules can meet the requirements of the preset verification threshold, thereby providing more comprehensive and reliable security protection for detonation operations.

[0049] Throughout the implementation of the interlocking determination module, separating permission parameters from feature deviation parameters is fundamental, generating reasonable interlocking verification rules is the core, and adjusting and refining the rules based on the number of verification nodes is crucial to ensuring their effectiveness. Each step is interconnected and mutually influential, and only by accurately executing each step can scientific, reasonable, and effective interlocking verification rules be generated.

[0050] For example, when separating parameters, if the main operation authority and the secondary operation authority cannot be accurately distinguished, the generated interlocking verification rules may have deviations in the authority allocation; if the feature deviation parameters are not fully considered when generating the rules, the rules may be insufficiently adaptable to changes in iris features; and when the number of verification nodes covered by the rules is insufficient, if the adjacent nodes are not traversed in time and the authority indicators are not supplemented, the rules may have security loopholes and cannot effectively guarantee the safety of the detonation operation.

[0051] Through the above detailed implementation steps, the interlocking determination module can generate comprehensive and reliable interlocking verification rules based on the authority-related parameters in the iris feature vector, and adjust and improve the rules when necessary to ensure that in the three-person interlocking detonation scenario, the detonation operation can only be carried out when all relevant authorized personnel have passed the corresponding iris verification and met all verification rules. This implementation method fully reflects the security and rigor of the three-person interlocking mechanism. Through the precise management and dynamic adjustment of authority indicators and verification rules, it effectively prevents safety accidents caused by single-person misoperation or unauthorized operation, and provides a solid technical guarantee for the safe conduct of mining operations. In actual application, this implementation method can flexibly generate and adjust interlocking verification rules according to different mining operation scenarios and personnel authority configurations, and has strong adaptability and reliability.

[0052] Example 4:

[0053] This embodiment relates to a specific implementation of a pattern matching module. This module, in a mine-used wireless transmission iris recognition three-person interlock detonation controller, undertakes the key functions of identifying the detonation permission mode, adaptively matching the authority-related indicators, and calculating the identity verification deviation. The specific implementation process is detailed below with reference to an example: Consider a scenario where a three-person interlock detonation system in a mine requires verification of the iris permissions of primary operator A, secondary operator B, and safety supervisor C. When the system boots up, the pattern matching module retrieves the characteristic factors and matching factors from the detonation permission model. For example, the permission model might specify a matching frequency characteristic factor of "high-frequency continuous verification," requiring primary operator A to complete three iris scans within 30 seconds. The matching factor for permission response rate might be "urgent response," requiring secondary operator B to respond within 5 seconds.

[0054] A pattern transfer network is constructed, linking the characteristic factors and matching factors. In this example, the system first identifies the periodicity of the characteristic factor "high-frequency continuous verification" and determines whether it fully matches the preset operation cycle (for example, the standard verification cycle for the blasting preparation phase is 25-35 seconds). If the current characteristic factor's period parameter is 30 seconds, which matches the preset period, the characteristic factor is set as the starting node of the pattern transfer network.

[0055] Calculate the matching correlation between the characteristic factors and the matching factors. For example, the correlation between the characteristic factor "high-frequency continuous verification" and the matching factor "emergency response" can be determined based on factors such as the probability of both occurring simultaneously in historical data and the logical relevance of the permission verification process. Assuming that the calculated support for emergency response by high-frequency verification is 80%, intermediate nodes and terminal nodes are generated from high to low correlation. Intermediate nodes may include "Main Operator High-Frequency Verification Path" and "Secondary Operator Rapid Response Path," with the terminal node being "Permission Combination Verification Completed."

[0056] When backtracking features of the terminal nodes, if the relevance of a terminal node (such as the relevance of security supervisor C's permission verification) falls below a preset matching threshold (e.g., 70%), the path weights are adjusted, and the path that meets the threshold is output as the final path. For example, if the relevance of security supervisor C's verification process in the initial path is only 65%, the system will traverse its adjacent permission nodes (such as the backup verification node) and recalculate the relevance until a path with a relevance of 75% is found as the final path.

[0057] After determining the mode transfer network, we begin calculating identity verification deviations. Taking the high-frequency verification node of primary operator A as an example, we calculate the mean characteristic factor and the range of the matching factor for this terminal node in the mode transfer network. Assuming the mean characteristic factor is 3 times / 30 seconds, the range of the matching factor is the difference (±1 second) between the actual response time (4-6 seconds) and the standard time (5 seconds). We also calculate the global covariance of the characteristic factors and matching factors of all node factors (primary, secondary, and supervisory nodes) to measure the correlation between them.

[0058] The difference between the mean characteristic factor of a single terminating node (e.g., primary operator A) (3 times / 30 seconds) and the mean characteristic factor of the adjacent node (secondary operator B) (2 times / 30 seconds) is 1 time / 30 seconds. Dividing this by the global covariance (assuming it is 0.5) yields a characteristic fluctuation coefficient of 2. Simultaneously, the ratio of the matching factor range (±1 second) to the global covariance is calculated as 2. The two are weighted and summed using preset weights (e.g., a characteristic fluctuation coefficient weight of 0.6 and a matching factor ratio weight of 0.4). The resulting identity verification deviation for this node is 2 × 0.6 + 2 × 0.4 = 2.

[0059] If the actual response time during the verification process of deputy operator B is 7 seconds, which exceeds the 5-second requirement of the matching factor "emergency response", the matching factor range becomes ±2 seconds, the ratio to the global covariance is 4, the characteristic fluctuation coefficient is still 2, and the weighted summation of the identity verification deviation is 2×0.6+4×0.4=2.8, indicating that the verification deviation of this node is large and an additional verification process needs to be triggered.

[0060] In another scenario, if the authorization verification mode for safety supervisor C is "periodic spot check," the characteristic factor period is 120 seconds, and the preset operation period is 100-140 seconds, the starting node is set if the two match. If the matching factor is "regular response" (response time ≤ 10 seconds), and the correlation between it and the characteristic factor is calculated to be 75%, which is higher than the threshold of 70%, then the intermediate node "periodic spot check path" is generated, and the ending node is "spot check verification completed." During backtracking, if the correlation meets the requirement, the path is directly output.

[0061] When calculating the identity verification deviation of this node, it is assumed that the mean characteristic factor is 1 time / 120 seconds, the mean of the adjacent node (main operator A) is 3 times / 30 seconds, and the difference is -2 times / 30 seconds. Dividing it by the global covariance of 0.5, the characteristic fluctuation coefficient is -4; the matching factor range is the difference between the actual response time (8-10 seconds) and the standard time by ±2 seconds, and the ratio with the global covariance is 4. The weighted summation (weights are the same as above) gives a deviation of (-4)×0.6+4×0.4=-0.8, indicating that the verification deviation of this node is negative, that is, the response time is better than the standard value, but it needs to be comprehensively judged in combination with other nodes.

[0062] Throughout the implementation process, the pattern matching module constructs a dynamic pattern transition network by acquiring characteristic factors and matching factors for specific operational scenarios. It then calculates identity verification deviations at each node based on network parameters, achieving adaptive matching of permission-related indicators. For example, in a three-person interlocking system, different modes—high-frequency verification of the primary operator, rapid response of the secondary operator, and periodic spot checks for safety supervision—are all analyzed through correlation analysis of characteristic factors and matching factors. This results in corresponding verification paths within the pattern transition network, and deviation calculation ensures the accuracy of verification at each node.

[0063] This implementation method can flexibly adjust the matching strategy based on the authority characteristics of different positions in mining operations and the safety requirements of blasting scenarios. For example, in emergency blasting scenarios, the weight of the "emergency response" matching factor is increased to shorten the verification path; in routine operations, the verification path is generated according to the standard cycle to ensure that the authority verification meets safety requirements without affecting operational efficiency. Through path construction, correlation calculation and deviation analysis in specific examples, the module can accurately identify the detonation permission mode, provide reliable deviation data for the subsequent benchmark calibration module, and ultimately ensure the effectiveness of the three-person interlock detonation control scheme.

[0064] Example 5:

[0065] This embodiment involves a specific implementation method of the benchmark calibration module deducing the optimal verification threshold and the execution control module integrating the control scheme, which is detailed as follows with reference to a specific example in a mine blasting operation scenario: Consider an underground blasting operation at a mine. A three-person interlocked detonation system needs to verify the iris permissions of the primary operator, assistant operator, and safety supervisor. When the primary operator performs an iris scan, the system detects the current identity verification deviation, at which point the benchmark calibration module begins deriving the optimal verification threshold. For example, if the current identity verification deviation manifests as a fluctuation in the feature matching coefficient—for example, if the matching coefficient of a particular iris feature node of the primary operator differs from the standard value in the biometric database—the module needs to extract the verification pattern from historical data that most closely matches the current identity verification deviation.

[0066] Suppose a similar deviation occurred in the historical data. The verification mode at the time was a two-person verification mode during the blasting preparation phase, while the current scenario is the formal blasting phase with three people interlocked. The module calculates the Euclidean distance between the current deviation and the historical verification mode in terms of feature distribution. For example, the feature distribution of the current deviation involves the offset of three key feature points, while the offsets of the corresponding feature points in the historical mode are 2, 3, and 1, respectively. The Euclidean distance between the two is calculated as the first calibration reference value. At the same time, the difference in the number of feature points between the current identity verification deviation and the historical verification mode is calculated. The current scenario has five feature points involved in the verification, while the historical mode has four, resulting in a difference of 1, which is used as the second calibration reference value.

[0067] Based on the linear combination of the first and second calibration reference values, the module matches the optimal verification threshold in the preset verification threshold table. For example, in the preset verification threshold table, for the three-person interlocked formal blasting phase, when the first calibration reference value is within a certain range and the second calibration reference value is 1, the corresponding optimal verification threshold is 0.85. At this point, the baseline calibration module uses this threshold as the current verification standard and generates an authority deviation sequence by comparing the current iris feature value with the optimal verification threshold. For example, of the five feature points of the main operator, the deviations of three feature points are within the threshold range, and two exceed it, forming a corresponding authority deviation sequence.

[0068] The execution control module parses this permission deviation sequence and divides it into positive and negative permission intervals based on the node's identity verification direction. For example, during the primary operator's verification process, if three feature points have positive deviations (i.e., feature matching exceeds a threshold) and two have negative deviations (matching falls below a threshold), then a positive permission interval and a negative permission interval are created. The module then extracts the rate of decrease in permission deviations within the positive permission interval and the rate of increase in permission deviations within the negative permission interval. For example, in the positive interval, the rate of decrease from 0.9 to 0.85 is 0.02 per second, while in the negative interval, the rate of increase from 0.85 to 0.88 is 0.015 per second.

[0069] The two are weighted and fused according to the operation weights of the iris feature nodes to generate the control scheme's permission parameters. For example, if the primary operator's key feature node operation weight is 0.6, and the secondary node's is 0.4, the weighted value of the positive decay rate is 0.02 × 0.6 + 0.02 × 0.4 = 0.02, and the weighted value of the negative amplification rate is 0.015 × 0.6 + 0.015 × 0.4 = 0.015. These combined values generate the permission parameters used to determine whether the primary operator is allowed to continue participating in the interlocked detonation process.

[0070] When the assistant operator performs iris verification, the benchmark calibration module extracts the closest verification pattern from historical data, which is from a certain equipment commissioning phase. The current scenario is normal operation. After calculating the Euclidean distance of the feature distribution and the difference in the number of feature points, the optimal verification threshold is found to be 0.9. The assistant operator's iris feature values are compared with this threshold to generate an authorization deviation sequence, with four feature points within the threshold and one exceeding it. The execution control module divides the interval into positive and negative directions, extracting a positive deviation decay rate of 0.01 per second and a negative deviation increase rate of 0.03 per second. After weighted fusion based on the job weight, the authorization parameters are generated to determine whether the assistant operator's verification has passed.

[0071] During the safety supervisor verification process, assuming the current identity verification deviation is similar to the deviation of a specific emergency evacuation scenario in historical data, the first and second calibration reference values are calculated, and the optimal verification threshold is matched to 0.75. After comparison, the permission deviation sequence is generated. All feature points are within the threshold range, with no attenuation in the positive permission range and no increase in the negative range. After weighted fusion, the permission parameters indicate verification is passed.

[0072] The execution control module integrates the permission deviation sequence of the three people to form the final three-person interlock detonation control scheme for mine-use wireless transmission iris recognition. For example, if the primary operator and safety supervisor pass verification, but a feature point deviation of the secondary operator exceeds the threshold, the control scheme may require the secondary operator to rescan their iris or initiate a backup verification process until all permission deviations meet the requirements, ensuring the security of the three-person interlock mechanism.

[0073] In another scenario, if the verification deviation of the main operator is large, the optimal verification threshold matched by the benchmark calibration module is 0.8. The authority deviation sequence generated after comparison shows that multiple feature points exceed the threshold. The negative authority interval divided by the execution control module has a high increase rate. After weighted by the operation weight, the authority parameters do not meet the requirements. The control scheme will prohibit the main operator's authority, prevent the detonation operation, and avoid safety accidents.

[0074] Throughout the implementation process, the benchmark calibration module dynamically derives the optimal verification threshold for the current scenario through historical data comparison and reference value calculation, ensuring the rationality of the verification standard. The execution control module scientifically integrates and generates a control plan based on the permission deviation sequence and operation weights, achieving precise control of the three-person interlock detonation. This implementation method can adapt to the changing iris characteristics of different scenarios and personnel in mining operations. Through dynamic threshold calibration and permission deviation integration, it ensures the safety and reliability of detonation operations and avoids misoperation caused by verification deviations of a single person.

[0075] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0076] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A mine-used wireless transmission iris recognition three-person interlocking detonation controller, characterized in that: include: The iris acquisition module is used to obtain the iris image signal of the operator and set the identity verification interval corresponding to the three-person interlocking scenario; The identity verification module is used to divide the identity verification interval into multiple iris feature nodes, extract features from the iris data of each node, and generate an iris feature vector corresponding to the node; The interlock determination module is used to extract the permission association index from the iris feature vector, establish the interlock verification rule associated with the iris feature node, and obtain the permission confirmation parameter corresponding to the rule; The pattern matching module is used to identify the detonation permission pattern in the permission confirmation parameters, adaptively match the permission association indicators according to the permission pattern, and calculate the identity verification deviation of each node under different matching strategies; The benchmark calibration module is used to derive the optimal verification threshold based on the identity verification deviation and generate the permission deviation sequence by comparing the current iris feature value with the optimal verification threshold; The execution control module is used to parse the permission deviation sequence and integrate the permission deviation sequence into a mine-used wireless transmission iris recognition three-person interlocking detonation control solution based on the node's identity verification direction.

2. A mine-used wireless transmission iris recognition three-person interlock detonation controller according to claim 1, characterized in that: The implementation method of the identity verification module includes: constructing a biometric feature library corresponding to the iris feature node, the biometric feature library containing the iris image signal and the feature parameter vector of the iris data mapping; Perform feature point matching on the feature parameter vector, and divide the iris verification interval of the feature parameter vector according to the matching result; extract the feature critical point of the iris data from the iris verification interval, and set the feature critical point as the iris feature node.

3. The mine-used wireless transmission iris recognition three-person interlock detonation controller according to claim 2 is characterized in that: The iris verification interval for dividing the feature parameter vector also includes: Extract image clarity, feature matching rate, and biometric parameters based on the iris type and operation stage in the feature parameter vector, and generate a biometric tag based on the above parameters; The biometric tags are associated with the feature parameter vectors, and the iris equivalence between the tags is calculated. The feature parameter vectors with equivalence higher than the preset iris threshold are selected to form the iris verification interval.

4. The mine-used wireless transmission iris recognition three-person interlock detonation controller according to claim 1 is characterized in that: The implementation methods of generating the iris feature vector corresponding to the node include: For each iris feature node, obtain the node's feature matching data within a preset period based on the node's temporal position in the identity verification interval, and calculate the node's feature matching coefficient; When the feature matching coefficient exceeds the first verification threshold, the node is marked as a high-frequency verification node, and its iris data is extracted to form an iris feature vector; when the feature matching coefficient is lower than the first verification threshold, the node is marked as a steady-state node, and the iris data of the node's adjacent nodes are feature superimposed, and the superimposed data is reconstructed into an iris feature vector.

5. The mine-used wireless transmission iris recognition three-person interlock detonation controller according to claim 1 is characterized in that: The implementation of the interlocking determination module includes: Separate the main operation authority, the secondary operation authority and the feature deviation parameters from the iris feature vector, and generate the interlocking verification rules of the iris feature node based on the above parameters; If the number of verification nodes covered by the current interlocking verification rule is less than the preset verification threshold, the iris feature vectors of adjacent iris feature nodes are traversed, and the permission indicators not included in the verification rules of the adjacent nodes are added to the current rule.

6. The mine-used wireless transmission iris recognition three-person interlock detonation controller according to claim 1 is characterized in that: The implementation method of the pattern matching module includes: obtaining the characteristic factor of the matching frequency and the matching factor of the authority response rate in the detonation permission pattern; A pattern transition network associated with characteristic factors and matching factors is constructed, and the identity verification bias under different matching strategies is determined based on the transition probability of each path in the network.

7. The mine-used wireless transmission iris recognition three-person interlock detonation controller according to claim 6 is characterized in that: Building a mode transfer network also includes: Identify the periodic characteristics of the characteristic factor. If the current periodic characteristics completely match the preset operation period, then set the characteristic factor as the starting node of the mode transfer network. Calculate the matching correlation between the characteristic factor and the matching factor, and generate the intermediate nodes and terminal nodes of the pattern transfer network in descending order of correlation; The terminal node is backtracked, and when the correlation degree of the terminal node is lower than the preset matching threshold, it is output as the final path of the pattern transfer network.

8. The mine-used wireless transmission iris recognition three-person interlock detonation controller according to claim 7 is characterized in that: Calculation of identity verification deviation can be achieved by: Statistically calculate the mean value of the characteristic factor and the range of the matching factor of each terminal node in the pattern transfer network, and calculate the global covariance of all node factors; The characteristic fluctuation coefficient is obtained by subtracting the mean characteristic factor of a single terminal node from the mean characteristic factor of the adjacent nodes and dividing it by the global covariance. At the same time, the ratio of the matching factor range to the global covariance is calculated, and the weighted sum of the two is used as the identity verification deviation of the node.

9. The mine-used wireless transmission iris recognition three-person interlock detonation controller according to claim 1 is characterized in that: The implementation of deriving the optimal verification threshold includes: Extract the verification mode from the historical data that is closest to the current identity verification deviation, and calculate the Euclidean distance between the two in the feature distribution as the first calibration reference value; Count the difference in the number of feature points between the current identity verification deviation and the historical verification mode, and use the difference as the second calibration reference value; Based on the linear combination of the first calibration reference value and the second calibration reference value, an optimal verification threshold in a preset verification threshold table is matched.

10. The mine-used wireless transmission iris recognition three-person interlock detonation controller according to claim 1 is characterized in that: The implementation of the execution control module includes: dividing the positive permission interval and the negative permission interval according to the identity verification direction of each node in the permission deviation sequence; The attenuation rate of the authority deviation in the positive authority interval and the increase rate of the authority deviation in the negative authority interval are extracted, and the two are weightedly fused according to the operation weight of the iris feature node to generate the authority parameters of the mine-used wireless transmission iris recognition three-person interlocking detonation control scheme.

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