A mine group digital production operation management and control system and method

Abstract

This invention discloses a digital production operation and control system and method for mining groups, relating to the field of digital production management technology in mining. The system includes a mining group control center generating structured control commands, performing security verification and signature verification via blockchain smart contracts, generating authorized command packages, and transmitting them to target mine edge nodes. The mine edge nodes receive the authorized command packages, collect real-time spatial gravitational changes, calculate gravitational distortion rates, and input them into a chaotic synchronization discriminator to generate an anomaly monitoring dataset. Based on the gravitational distortion rate sequence in the anomaly monitoring dataset, a hierarchical response mechanism is triggered to generate hierarchical control command packages. This invention achieves a high level of security, not only improving the anti-tampering capability during command transmission but also enhancing overall security.

Description

Technical Field

[0001] This invention relates to the field of digital production management technology in mines, and in particular to a digital production operation control system and method for mining groups. Background Technology

[0002] With the rapid development of information technology, digital transformation in the mining industry has gradually become an important way to improve production efficiency, optimize resource allocation, and ensure safe production. In recent years, the application of emerging technologies such as the Internet of Things (IoT), big data analytics, and cloud computing has provided technical support for intelligent mining, enabling mining companies to more effectively collect and process massive amounts of data and use data analysis to assist in decision-making. Furthermore, blockchain technology, with its decentralized and tamper-proof characteristics, has shown great potential in ensuring information security and data transparency, especially in cross-regional and multi-level information exchange within mining groups, providing the possibility for building an efficient and secure data sharing mechanism.

[0003] Existing digital management systems for mines often lack effective predictive models and response mechanisms when dealing with sudden geological disasters or abnormal situations, resulting in the inability to take timely measures to prevent accidents. Furthermore, most current systems fail to fully utilize the advantages of cutting-edge technologies such as quantum computing, and the speed and accuracy of data processing in complex environments need improvement. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a digital production operation and management system and method for mining groups, which solves the problems of insufficient prediction capability and lagging response mechanism of existing systems in complex geological environments.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a digital production operation and management method for a mining group, comprising: the mining group control center generating structured control instructions, performing security verification and signature verification through a blockchain smart contract, generating an authorized instruction package and transmitting it to the target mine edge node; the mine edge node receiving the authorized instruction package, collecting spatial gravitational changes in real time and calculating the gravitational distortion rate, and simultaneously inputting it into a chaotic synchronization discriminator to generate an anomaly monitoring dataset; triggering a hierarchical response mechanism based on the gravitational distortion rate sequence in the anomaly monitoring dataset to generate a hierarchical control instruction package; parsing the hierarchical control instruction package through a spiking neural network to generate a set of safe execution parameters, and driving support equipment, ventilation equipment, and power equipment, while simultaneously sending collaborative instructions to adjacent mines to generate equipment execution feedback data; associating the equipment execution feedback data with the authorized instruction package to generate a quantum entanglement hash value and verifying it, and simultaneously combining it with satellite remote sensing surface deformation data to generate a dynamic interactive comprehensive management and control report.

[0008] As a preferred embodiment of the digital production operation and management method for mining groups described in this invention, the mining group control center generates structured control instructions, which are then used for security verification and signature verification via blockchain smart contracts. The specific steps are as follows:

[0009] The mining group's control center generates structured control commands through a risk decision engine and inputs them into a quantum random number generator to generate dynamic keys;

[0010] The structured control commands are encrypted using a dynamic key and the national cryptographic algorithm to form encrypted commands.

[0011] The encrypted instructions are submitted to the blockchain smart contract for cross-chain security verification and digital signature verification.

[0012] As a preferred embodiment of the digital production operation and management method for mining groups described in this invention, the specific steps for generating the authorization instruction package and transmitting it to the target mine edge node are as follows:

[0013] Once the security verification is successful, the signed encrypted instructions are combined with the quantum key hash to generate an authorization instruction package;

[0014] The authorized instruction packet is transmitted to the target mine edge node via a quantum key distribution protocol.

[0015] As a preferred embodiment of the digital production operation and management method for mining groups described in this invention, the mine edge node receives authorized instruction packets, collects spatial gravitational changes in real time, and calculates the gravitational distortion rate. The specific steps are as follows:

[0016] The edge node of the mine receives the authorized instruction packet, activates the quantum chaotic oscillator, and generates a chaotic carrier signal;

[0017] The laser phase of the gravity measurement device is modulated using a chaotic carrier signal to collect space gravitational gradient data in real time.

[0018] Dynamic analysis of time-varying tensor fields is performed on spatial gravitational gradient data to generate spacetime tensor field data and calculate gravitational distortion rate.

[0019] As a preferred embodiment of the digital production operation and management method for mining groups described in this invention, the steps for simultaneously inputting a chaotic synchronization discriminator to generate an anomaly monitoring dataset are as follows:

[0020] The gravitational distortion rate is input into the chaos synchronization discriminator, and an anomaly is marked when the degree of chaos desynchronization exceeds the safety threshold in multiple consecutive samplings.

[0021] The spatiotemporal tensor field data marked with anomalies are compressed and stored to generate an anomaly monitoring dataset.

[0022] As a preferred embodiment of the digital production operation and management method for mining groups described in this invention, the step of triggering a hierarchical response mechanism based on the gravitational distortion rate sequence in the anomaly monitoring dataset to generate a hierarchical control instruction package includes the following specific steps:

[0023] A hypergraph state space is constructed based on the gravitational distortion rate sequence in the anomaly monitoring dataset, and a hypergraph adjacency matrix is ​​generated.

[0024] A quantum annealing Hamiltonian is constructed using the hypergraph adjacency matrix, and the optimal policy parameter vector is generated through a quantum annealing optimization algorithm.

[0025] Based on the optimal policy parameter vector and the gravitational distortion rate, the dynamic risk index is calculated by a linear combination of the spatiotemporal weighted integral and the policy weighted rate of change.

[0026] The response level is determined based on the multi-level risk assessment threshold range in which the dynamic risk index falls, and a graded control instruction package is generated.

[0027] As a preferred embodiment of the digital production operation and management method for mining groups described in this invention, the specific steps for generating a set of safe execution parameters by parsing hierarchical control instruction packets using a spiking neural network are as follows:

[0028] The hierarchical control instruction package is parsed using the spiking neural network of a neuromorphic chip to generate a device control intent vector;

[0029] The device control intent vector is input into the digital twin platform for dynamic simulation verification, generating a set of safe execution parameters.

[0030] As a preferred embodiment of the digital production operation and management method for mining groups described in this invention, the drive support equipment, ventilation equipment, and power equipment simultaneously send collaborative instructions to adjacent mines and generate equipment execution feedback data. The specific steps are as follows:

[0031] The safety execution parameter set is compiled into a device control signal stream through an FPGA hardware acceleration card, which drives the support equipment, ventilation equipment and power equipment in real time, and collects equipment operation status data in real time.

[0032] By distributing collaborative instructions to neighboring mines through a service mesh architecture, blockchain-based evidence is generated.

[0033] By integrating equipment operation status data with blockchain-based evidence, equipment execution feedback data is generated.

[0034] As a preferred embodiment of the digital production operation and management method for mining groups described in this invention, the steps of associating equipment execution feedback data with authorized instruction packets to generate and verify quantum entangled hash values, and simultaneously generating a dynamic interactive comprehensive management and control report by combining satellite remote sensing surface deformation data, are as follows:

[0035] Based on the device execution feedback data and authorization instruction package, a quantum entanglement hash value is generated;

[0036] Acquire satellite remote sensing data on land surface deformation and generate a land surface deformation field through phase unwrapping and atmospheric correction;

[0037] The quantum entanglement hash value is spatiotemporally coupled with the surface deformation field for verification, and then verified through a quantum blockchain hybrid consensus mechanism.

[0038] Based on the verification results, a comprehensive risk index is obtained, and a dynamic, interactive comprehensive management and control report is generated through a holographic rendering engine.

[0039] Secondly, this invention provides a digital production operation and management system for a mining group, comprising an instruction generation module, an anomaly monitoring module, a response control module, an equipment driving module, and a comprehensive evaluation module. The instruction generation module is used by the mining group control center to generate structured control instructions, perform security verification and signature verification through blockchain smart contracts, generate authorized instruction packages, and transmit them to the target mine edge nodes. The anomaly monitoring module is used by the mine edge nodes to receive the authorized instruction packages, collect real-time spatial gravitational changes, calculate the gravitational distortion rate, and input the data into a chaotic synchronization discriminator to generate an anomaly monitoring dataset. The response control module is used to trigger a hierarchical response mechanism based on the gravitational distortion rate sequence in the anomaly monitoring dataset, generating hierarchical control instruction packages. The equipment driving module is used to parse the hierarchical control instruction packages through a spiking neural network, generate a set of safe execution parameters, drive support equipment, ventilation equipment, and power equipment, and simultaneously send collaborative instructions to adjacent mines, generating equipment execution feedback data. The comprehensive evaluation module is used to associate the equipment execution feedback data with the authorized instruction packages, generate a quantum entanglement hash value for verification, and, in conjunction with satellite remote sensing surface deformation data, generate a dynamic interactive comprehensive management and control report.

[0040] The beneficial effects of this invention are as follows: By generating structured control instructions through the mining group control center and verifying and signing them using blockchain smart contracts, a high level of security is achieved, which not only improves the anti-tampering capability during instruction transmission but also enhances overall security; by triggering a hierarchical response mechanism based on anomaly monitoring datasets and generating hierarchical control instruction packages, not only is the early warning capability for potential geological disasters improved, but effective preventive measures can also be taken before disasters occur, greatly reducing the probability of accidents. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of a digital production operation and management method for a mining group.

[0043] Figure 2 A flowchart for the generation and transmission of authorization instruction packets.

[0044] Figure 3 A flowchart generated for the anomaly monitoring dataset.

[0045] Figure 4 A flowchart for generating hierarchical control instruction packages. Detailed Implementation

[0046] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0047] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0049] Reference Figure 1 As one embodiment of the present invention, this embodiment provides a digital production operation management and control method for a mining group, including the following steps:

[0050] S1: The mining group's control center generates structured control instructions, performs security verification and signature verification through blockchain smart contracts, generates an authorization instruction package, and transmits it to the target mine's edge node. For details, please refer to [link to relevant documentation]. Figure 2 .

[0051] The mining group's control center generates structured control instructions through a risk decision engine and inputs them into a quantum random number generator to generate dynamic keys.

[0052] The specific process involves the mining group's control center's risk decision engine analyzing real-time operating parameters and historical operation records. Combining this with preset safety thresholds and optimization goals, it generates structured control instructions containing equipment adjustment parameters and priority markers. These structured control instructions are transmitted via an encrypted channel to a quantum random number generator. The generator uses quantum tunneling to produce a true random number sequence. This sequence is then hashed with the timestamps of the structured control instructions to generate a dynamic key. This dynamic key is immediately bound to the current batch of structured control instructions, ensuring the integrity and immutability of instruction transmission and execution.

[0053] The preset safety threshold is a dynamic critical value determined through multi-dimensional statistical analysis based on the geological structure characteristics of the mine, historical equipment operation data, stability parameters of the quantum chaotic oscillator, and industry safety standards.

[0054] Structured control commands are encrypted using a dynamic key and a national cryptographic algorithm to form encrypted commands.

[0055] The specific process includes: the dynamic key is input as an encryption factor into the national cryptographic algorithm; the national cryptographic algorithm uses a symmetric encryption mode to obfuscate and diffuse the structured control instructions byte by byte, generating ciphertext data blocks that conform to the standards of the State Cryptography Administration. The ciphertext data blocks are combined with the checksum of the dynamic key to form the encrypted instructions.

[0056] The encrypted instructions are submitted to the blockchain smart contract for cross-chain security verification and digital signature verification.

[0057] The specific process includes: the encrypted command is transmitted to the blockchain smart contract via a cross-chain communication protocol; the blockchain smart contract calls pre-built verification logic to parse the encrypted command; first, it verifies whether the digital signature matches the identity certificate of the mining group's control center; then, it verifies the legality and timeliness of the dynamic key through a cross-chain relay. After the blockchain smart contract executes a consensus algorithm to confirm the integrity and authenticity of the encrypted command, it records the verification result in the blockchain distributed ledger, adds a timestamp, generates a block hash value containing a verification pass marker, and returns it to the mining group's control center.

[0058] The pre-defined verification logic is implemented through smart contract code, based on encryption algorithm standards, blockchain cross-chain communication protocol specifications, and the security policy requirements of the mining group's control center.

[0059] Once the security verification is successful, the signed encrypted instructions are combined with the quantum key hash to generate an authorization instruction package.

[0060] The specific process includes: after the blockchain smart contract is verified, the signed encrypted instructions are hashed with a dynamic key generated by a quantum random number generator to generate a unique quantum key hash value. The signed encrypted instructions and the quantum key hash value are combined and packaged according to a predefined data format to form an authorization instruction package containing complete verification information and encrypted content. The authorization instruction package is transmitted to the execution terminal through a secure channel to ensure the verifiability and non-repudiation of the instruction transmission process.

[0061] The predefined data format is determined through structured coding based on data encapsulation specifications, blockchain smart contract interaction protocols, and instruction transmission requirements of the mining group control center.

[0062] The authorized instruction packet is transmitted to the target mine edge node via a quantum key distribution protocol.

[0063] The specific process includes: the authorization instruction packet is transmitted through a secure channel established by a quantum key distribution protocol. This protocol utilizes the no-cloning property of quantum states to generate a shared key between the mining group's control center and the target mine's edge nodes. The shared key is used to encrypt the authorization instruction packet frame by frame before it is sent to the target mine's edge nodes via a classical communication channel. The target mine's edge nodes use the same quantum key distribution protocol to decrypt and restore the authorization instruction packet. During decryption, quantum bit measurement basis comparison ensures that the transmission process is not eavesdropped on, and the quantum key hash value and signature verification information of the authorization instruction packet are fully preserved.

[0064] S2: The mine edge node receives the authorized command packet, collects the spatial gravitational changes in real time and calculates the gravitational distortion rate, and simultaneously inputs it into the chaotic synchronization discriminator to generate an anomaly monitoring dataset. For details, please refer to [link to relevant documentation]. Figure 3 .

[0065] The edge node of the mine receives the authorized instruction packet, activates the quantum chaotic oscillator, and generates a chaotic carrier signal.

[0066] The specific process involves the following steps: After successfully receiving the authorization command packet, the target mine edge node immediately activates its built-in quantum chaotic oscillator. The quantum chaotic oscillator generates a chaotic carrier signal with unpredictable characteristics using nonlinear quantum dynamics principles. The frequency and phase of the chaotic carrier signal are modulated by the quantum key hash value in the authorization command packet, forming a carrier waveform uniquely corresponding to the authorization command packet. The modulated chaotic carrier signal is then radiated to the mine equipment array through a quantum channel, providing a synchronization reference and a security carrier for subsequent command execution.

[0067] The laser phase of the gravity measurement device is modulated by a chaotic carrier signal to collect space gravitational gradient data in real time.

[0068] The specific process involves inputting a chaotic carrier signal generated by a quantum chaotic oscillator into the laser interferometer array of the gravity measurement device. The chaotic carrier signal nonlinearly modulates the phase of the measurement laser using a piezoelectric crystal. The modulated measurement laser is emitted to a preset monitoring area, and the reflected laser carries the spatial gravitational gradient information back to the interferometer. The interferometer coherently demodulates the chaotic carrier signal and the returned laser. The differential signal output from the demodulation is then processed by Fourier transform to generate real-time spatial gravitational gradient data. The spatial gravitational gradient data and the chaotic carrier signal maintain strict synchronization in terms of timing characteristics, ensuring the measurement results' anti-interference capabilities and timing consistency.

[0069] Dynamic analysis of time-varying tensor fields is performed on spatial gravitational gradient data to generate spacetime tensor field data and calculate the gravitational distortion rate, expressed as:

[0070] ;

[0071] in, Represents the gravitational distortion rate. Indicates the integration time window. The spatial domain representing the three-dimensional monitoring area. Represents spatial derivative operations. Denotes the Frobenius norm. Represents a spatial location coordinate vector. Represents a time variable.

[0072] in, The gravitational tensor field is expressed as:

[0073] ;

[0074] It should be noted that, Indicates the total number of sensors. Indicates the sensor number, Indicates the first The gravitational gradient vector measured by each sensor. Represents the outer product of vectors. Indicates the first Spatial coordinates of each sensor This represents the field attenuation coefficient.

[0075] in, The electromagnetic interference tensor field is expressed as:

[0076]

[0077] It should be noted that, Indicates the electromagnetic interference suppression factor. Represents the geomagnetic field intensity vector. Indicates the reference time constant. This represents the reference geomagnetic field constant.

[0078] The specific process includes processing spatial gravitational gradient data using a time-varying tensor field dynamic analytical algorithm. This algorithm first constructs a gravitational tensor field, generated by superimposing the gravitational gradient vectors from multiple sensors with their spatial coordinates through a vector outer product operation. The superposition process considers the weighting adjustment of the field attenuation coefficient for far-field data. Simultaneously, an electromagnetic interference tensor field is calculated, normalized based on the difference between the geomagnetic field intensity vector and the reference geomagnetic field constant, combined with an electromagnetic interference suppression factor. The gravitational and electromagnetic interference tensors are then subjected to tensor operations in the spatiotemporal domain to generate a net gravitational tensor field. This net gravitational tensor field undergoes spatial derivative and Frobenius norm calculations, and is then spatiotemporally integrated over the three-dimensional monitoring area within an integration time window, ultimately outputting the gravitational distortion rate. The calculation of the gravitational distortion rate is strictly synchronized with the chaotic carrier signal to ensure the dynamic analytical accuracy of the spatiotemporal tensor field data.

[0079] The gravitational distortion rate is input into the chaotic synchronization discriminator, and an anomaly is marked when the degree of chaotic desynchronization exceeds the safety threshold in multiple consecutive samplings.

[0080] The specific process includes inputting the gravitational distortion rate into a chaotic synchronization discriminator in real time. The discriminator then performs phase coherence analysis on the gravitational distortion rate and the chaotic carrier signal generated by the quantum chaotic oscillator to obtain a chaotic desynchronization index. The chaotic synchronization discriminator continuously monitors the trend of chaotic desynchronization within a preset sampling period. When the chaotic desynchronization exceeds a preset safety threshold for multiple consecutive sampling periods, the discriminator generates an anomaly marker signal. This anomaly marker signal contains the anomaly start timestamp and the magnitude of the desynchronization exceedance, and is transmitted through a secure channel to the risk decision engine of the mining group's control center, triggering subsequent early warning and handling procedures. The entire discrimination process maintains time synchronization with the quantum key distribution protocol to ensure the timeliness and reliability of anomaly detection.

[0081] The preset sampling period was determined through experimental calibration and dynamic optimization based on the inherent frequency characteristics of the quantum chaotic oscillator, the response time of the gravity measurement equipment, and the real-time requirements of mine safety monitoring.

[0082] The spatiotemporal tensor field data marked with anomalies are compressed and stored to generate an anomaly monitoring dataset.

[0083] The specific process includes: after the chaotic synchronization discriminator marks an anomaly, it immediately initiates a compression and storage process for the spatiotemporal tensor field data during the anomaly period. A hybrid compression algorithm combining wavelet transform and Huffman coding is used to process the spatiotemporal tensor field data. The hybrid compression algorithm first performs multi-resolution wavelet decomposition on the spatiotemporal tensor field data, retains the main characteristic coefficients, and then uses Huffman coding to entropy-encoded compress the coefficient matrix. The compressed data block, along with the anomaly marker signal, timestamp, and gravitational distortion rate curve, is encapsulated into a structured data packet, generating an anomaly monitoring dataset. The anomaly monitoring dataset is transmitted to distributed storage nodes through a quantum encryption channel, and a blockchain digital fingerprint is attached during the storage process to ensure data immutability.

[0084] S3: Based on the gravitational distortion rate sequence in the anomaly monitoring dataset, a hierarchical response mechanism is triggered to generate a hierarchical control command package. Please refer to [link / reference] for details. Figure 4 .

[0085] The hypergraph state space is constructed based on the gravitational distortion rate sequence in the anomaly monitoring dataset, and the hypergraph adjacency matrix is ​​generated.

[0086] The specific process includes: after time-aligned normalization, outlier removal, sliding window smoothing filtering, and quantum timestamp calibration of the gravitational distortion rate sequence in the anomaly monitoring dataset, a hypergraph state space is constructed using a hypergraph modeling method. The hypergraph state space maps the gravitational distortion rate and its spatiotemporal features at each sampling time to hypergraph vertices. Hypergraph vertices are connected through multivariate relationships to form hyperedges, the weight of which is determined by the spatiotemporal correlation strength of the gravitational distortion rate. Based on the topological structure of the hypergraph state space, a joint similarity matrix between vertices is derived and transformed into a symmetric hypergraph adjacency matrix. The hypergraph adjacency matrix stores the nonlinear correlation features of the gravitational distortion rate in the spatiotemporal dimension, providing a structured representation for subsequent anomaly pattern recognition. The entire construction process preserves quantum timestamp information to ensure the spatiotemporal consistency between the hypergraph state space and the original monitoring data.

[0087] A quantum annealing Hamiltonian is constructed using the hypergraph adjacency matrix, and the optimal policy parameter vector is generated through a quantum annealing optimization algorithm.

[0088] The specific process includes mapping the hypergraph adjacency matrix as input parameters to the quantum annealing Hamiltonian, which transforms the asymmetric characteristics of the hypergraph adjacency matrix into spin interaction terms. After initializing the transverse magnetic field, the quantum annealing optimization algorithm gradually reduces the transverse field strength according to an adiabatic evolution path, causing the ground state of the quantum annealing Hamiltonian to converge to the optimal solution. During the annealing process, the algorithm monitors the energy tunneling effect in real time, reading the qubit configuration when the global energy reaches its minimum. After decoding, the qubit configuration is transformed into a continuous optimal policy parameter vector, which includes the amplitude-frequency characteristics and phase compensation quantities controlled by gravitational distortion. The entire quantum annealing process maintains quantum entanglement with the chaotic carrier signal, ensuring the spatiotemporal correlation of the optimal policy parameter vector.

[0089] Based on the optimal policy parameter vector and the gravitational distortion rate, the dynamic risk index is calculated through a linear combination of the spatiotemporal weighted integral and the policy weighted rate of change. The expression is as follows:

[0090] ;

[0091] in, This indicates a dynamic risk index. Represents the time variable of integration. Indicates a point in time The gravitational distortion rate at that location. Indicates the exponential decay factor. Represents the optimal policy parameter vector. The rate of change of gravitational distortion rate.

[0092] The specific process includes inputting the optimal strategy parameter vector and the real-time gravitational distortion rate into the dynamic risk index calculation process. First, the gravitational distortion rate is integrated in the time domain, with historical data weighted using an exponential decay factor during the integration process. Simultaneously, the dot product of the optimal strategy parameter vector and the rate of change of the gravitational distortion rate is calculated as the strategy adjustment component. The spatiotemporal weighted integral result and the strategy weighted rate of change are synthesized using a linear combination formula, maintaining quantum timestamp synchronization during the synthesis process. The calculation result is output as a scalar value of the dynamic risk index, which quantitatively represents the comprehensive risk level in the current spatiotemporal domain. The dynamic risk index is fed back to the mining group's control center in real time through a quantum encrypted channel, providing a quantitative basis for the risk decision engine. The entire calculation process maintains phase lock with the quantum chaotic oscillator to ensure the timeliness of risk assessment.

[0093] The response level is determined based on the multi-level risk assessment threshold range in which the dynamic risk index falls, and a graded control instruction package is generated.

[0094] The specific process includes comparing a dynamic risk index input with a multi-level risk assessment threshold range, which is divided into multiple continuous numerical ranges according to mine safety regulations. After determining the response level based on the comparison results, the risk decision engine calls a pre-set response strategy template to generate a tiered control instruction package. The tiered control instruction package contains a set of equipment adjustment parameters, priority markers, and a quantum timestamp. The tiered control instructions automatically match the adjustment amplitude and response speed according to the response level. The tiered control instruction package is encrypted using a quantum key distribution protocol and transmitted to the target device node, while also attaching a digital signature generated by a blockchain smart contract. The entire tiered response process maintains spatiotemporal synchronization with the chaotic carrier signal to ensure the accuracy of matching control instructions with the real-time risk status.

[0095] The multi-level risk assessment threshold is a dynamic grading standard determined by machine learning optimization based on the mine's geomechanical characteristics, historical accident statistics, equipment operating parameters, quantum chaotic stability indicators, and industry safety standards.

[0096] The pre-built response strategy template is constructed in advance using the quantum decision tree algorithm based on the performance parameters of mining equipment, historical emergency response records, dynamic risk transmission models, and industry safety operating procedures.

[0097] S4: It analyzes the hierarchical control instruction package through a spiking neural network, generates a set of safe execution parameters, drives the support equipment, ventilation equipment and power equipment, and sends collaborative instructions to adjacent mines to generate equipment execution feedback data.

[0098] The hierarchical control instruction package is parsed using a spiking neural network of a neuromorphic chip to generate a device control intent vector.

[0099] The specific process includes: after the hierarchical control command package is input into the neuromorphic chip, the spiking neural network of the neuromorphic chip first decrypts and restores the encrypted commands. The decrypted hierarchical control command package is mapped into a pulse timing pattern through a synaptic weight matrix. The spiking neural network activates corresponding neuron clusters according to the command priority, and the neuron clusters encode device control parameters through pulse firing frequency. After the pulse timing pattern is converted by an integral-firing mechanism, a device control intent vector is generated. The device control intent vector contains normalized control amplitude and response timing characteristics. The generation process maintains pulse synchronization with the quantum chaotic oscillator to ensure the real-time matching accuracy between the device control intent vector and the risk level. The device control intent vector is output to the actuator through the neuromorphic interface to complete the closed-loop control process.

[0100] The device control intent vector is input into the digital twin platform for dynamic simulation verification, generating a set of safe execution parameters.

[0101] The specific process includes: after the equipment control intent vector is transmitted to the digital twin platform, the digital twin platform calls the mining equipment dynamics framework to perform multiphysics coupling simulation. The mining equipment dynamics framework analyzes the motion parameters in the equipment control intent vector based on the Lagrange equations and constructs a virtual execution scenario by combining real-time collected stress-strain field data. The simulation process uses an explicit time integration algorithm to calculate the dynamic response of each actuator, and employs a continuous collision detection algorithm combined with a spatiotemporal bounding box hierarchy to analyze the Euclidean distance field between the support equipment's motion trajectory and the geological structure point cloud data in real time. When the minimum distance threshold exceeds the dynamic safety margin, it is determined to be a safe trajectory. The verification results are then subjected to Monte Carlo risk assessment to generate a safe execution parameter set, which includes optimized velocity curves, force thresholds, and tolerance ranges. The digital twin platform compares and calibrates the safe execution parameter set with the equipment control intent vector to ensure that the output parameters meet quantum time synchronization requirements and dynamic stability constraints. The calibrated safe execution parameter set is then signed via a blockchain smart contract and returned to the neuromorphic chip, completing the verification loop.

[0102] The dynamic safety margin is dynamically set according to the gravitational distortion rate-stress coupling formula, and the optimal safety threshold range is obtained in real time through the quantum annealing optimization algorithm.

[0103] The force threshold is set based on the coupling relationship between the device's dynamic limit and the real-time gravitational distortion rate, and is specifically determined through dynamic optimization of Monte Carlo risk assessment on a digital twin platform.

[0104] The tolerance range is set dynamically based on the coupling between equipment control precision and gravitational distortion rate, and is specifically determined in real time through parameter sensitivity analysis of the digital twin platform.

[0105] The safety execution parameter set is compiled into a device control signal stream through an FPGA hardware acceleration card, which drives the support equipment, ventilation equipment and power equipment in real time, and collects equipment operation status data in real time.

[0106] The specific process includes: after the safety execution parameter set is input into the FPGA hardware acceleration card, the FPGA hardware acceleration card converts the safety execution parameter set into a register-transfer level logic circuit for parallel processing using a hardware description language. The register-transfer level logic circuit generates a device control signal stream containing pulse-width modulation waveforms and digital trigger sequences. The device control signal stream is then synchronously output to the hydraulic controller of the support equipment, the frequency converter of the ventilation equipment, and the intelligent circuit breaker of the power equipment after opto-isolation. During the driving process, data from the pressure sensor of the support equipment, the anemometer reading of the ventilation equipment, and the power monitoring value of the power equipment are collected in real time. The collected device operating status data is temporarily stored in the FPGA hardware acceleration card's first-in-first-out memory after high-speed ADC conversion. The device control signal stream maintains strict time synchronization with the device operating status data, and each control cycle is phase-locked with the chaotic carrier signal of the quantum chaotic oscillator. The FPGA hardware acceleration card continuously compares the device operating status data with the expected values ​​of the safety execution parameter set and adjusts the device control signal stream parameters in real time through dynamic reconfiguration technology. After the complete device operating status dataset is timestamped and blockchain verified, it is transmitted back to the mining group control center through a quantum encryption channel.

[0107] By distributing collaborative instructions to neighboring mines through a service mesh architecture, blockchain-based evidence is generated.

[0108] The specific process includes: the mining group control center encapsulates collaborative instructions into lightweight data packets using a service mesh architecture; the service mesh architecture enables cross-mine node communication based on a bidirectional TLS encrypted channel. When the collaborative instruction data packet is distributed to adjacent mine edge computing nodes, a blockchain smart contract is triggered. The blockchain smart contract hashes the instruction content and adds a timestamp and digital signature to generate a blockchain-based evidence certificate. The distribution process uses gRPC streaming to ensure real-time performance, and the service mesh architecture optimizes the transmission path through a load balancing algorithm. The blockchain evidence certificate is written to the consortium blockchain distributed ledger and simultaneously updated to the local ledger copies on each node. The entire collaborative process maintains quantum time synchronization, the blockchain evidence certificate uses a Merkle tree structure to ensure immutability, and the service mesh architecture continuously monitors node status and automatically retrys failed transmissions.

[0109] By integrating equipment operation status data with blockchain-based evidence, equipment execution feedback data is generated.

[0110] The specific process includes: a data fusion engine that integrates equipment operating status data generated during equipment execution with blockchain-based evidence input data; the engine performs time alignment processing on the equipment operating status data, achieving nanosecond-level synchronization based on quantum timestamps; after verification by a smart contract, the blockchain-based evidence extracts key audit fields; these fields are then tensor-concatenated with the aligned equipment operating status data in the feature space; the concatenated key audit fields and equipment operating status data undergo Kalman filtering to eliminate measurement noise; and the Kalman filter output is used to generate a data fingerprint through hash operations; the data fingerprint is combined with equipment status parameters to form equipment execution feedback data, which includes execution performance evaluation indicators and security audit traces; the generation process maintains consistency with the communication protocol of the service mesh architecture; and the equipment execution feedback data is returned to the mining group's control center via a quantum-encrypted channel, while simultaneously being written into the consortium blockchain to form a closed-loop record.

[0111] Key audit fields refer to the instruction execution metadata in the blockchain-based evidence storage certificate that has been verified by the smart contract, specifically including three elements: quantum timestamp, device control signal stream hash value, and dynamic risk index verification result.

[0112] S5: Associate the device's execution feedback data with the authorized instruction package, generate a quantum entanglement hash value and verify it, and at the same time combine it with satellite remote sensing surface deformation data to generate a dynamic interactive comprehensive management and control report.

[0113] Based on the device execution feedback data and authorization instruction package, a quantum entanglement hash value is generated.

[0114] The specific process includes inputting device execution feedback data and authorization instruction packets into a quantum hash generator. The quantum hash generator then performs bit-sequential concatenation between the security audit trace in the device execution feedback data and the quantum key hash value of the authorization instruction packet. The concatenated security audit trace and quantum key hash value generate a quantum superposition state through a quantum entanglement gate circuit. This quantum superposition state collapses into an entangled bit sequence under the action of a measurement basis. The entangled bit sequence undergoes a quantum Fourier transform to extract phase features. These phase features are then modulated with the instantaneous frequency of a chaotic carrier signal to generate a quantum entangled hash value. The quantum entangled hash value simultaneously carries the integrity characteristics of the device execution feedback data and the quantum fingerprint of the authorization instruction packet, and is transmitted to a blockchain smart contract for verification via a quantum key distribution protocol. The generation process strictly maintains phase coherence with the quantum chaotic oscillator, ensuring the unclonable and tamper-proof nature of the quantum entangled hash value.

[0115] We acquire satellite remote sensing data on land deformation and generate a land deformation field through phase unwrapping and atmospheric correction.

[0116] The specific process includes: after satellite remote sensing surface deformation data is acquired by ground receiving stations, it first undergoes interferogram generation processing, which obtains a preliminary deformation gradient based on the phase information of synthetic aperture radar imagery. A phase unwrapping algorithm resolves phase ambiguity in the interferogram, using a minimum cost flow method to recover the true phase value. Atmospheric correction processing eliminates atmospheric delay effects using water vapor radiometer data and meteorological station observations, converting the atmospherically corrected phase data into surface deformation data. The surface deformation data is then geocoded and interpolated using a grid to generate a surface deformation field, which includes vertical and horizontal deformation components. The processing strictly maintains time series consistency; a quantum timestamp is appended to the output surface deformation field and verified against a blockchain-based credential.

[0117] The quantum entanglement hash value is spatiotemporally coupled with the Earth's surface deformation field for verification, and then verified through a quantum blockchain hybrid consensus mechanism.

[0118] The specific process includes inputting the quantum entangled hash value and the surface deformation field into a spatiotemporal coupling verification algorithm. The algorithm performs spatiotemporal gridding on the surface deformation field, and the gridding result is precisely matched with the timestamp of the quantum entangled hash value. The matched data is transformed into a spatiotemporal feature matrix through Gram angle field transformation. This matrix is ​​then convolved with the phase features of the quantum entangled hash value using tensor convolution. The convolution result is input into a quantum blockchain hybrid consensus mechanism, which combines proof-of-work and quantum proof-of-stake for dual confirmation of the verification process. Verified quantum entangled hash values ​​generate new blocks and are written to the distributed ledger, simultaneously triggering a smart contract update of the device control strategy. The entire verification process maintains the quantum entanglement characteristics of the quantum key distribution protocol, ensuring the immutability and quantum security of the spatiotemporal coupling verification.

[0119] Based on the verification results, a comprehensive risk index is obtained, and a dynamic, interactive comprehensive management and control report is generated through a holographic rendering engine.

[0120] The specific process includes: inputting the verification results into a comprehensive risk index generation engine; the engine then integrates the quantum entanglement hash value verification results, satellite remote sensing surface deformation data, and equipment execution feedback data to generate a comprehensive risk index using a weighted aggregation algorithm. The comprehensive risk index is transmitted to a holographic rendering engine, which dynamically binds the comprehensive risk index to the three-dimensional geological structure of the mine using ray tracing technology. The bound comprehensive risk index and the three-dimensional geological structure are then converted into holographic projection parameters through voxelization. These parameters drive a laser interferometer array to generate a dynamic interactive comprehensive management and control report. This report includes a risk heatmap, equipment status markers, and control suggestion layers, supporting multi-angle naked-eye 3D interaction. The generation process of the dynamic interactive comprehensive management and control report is synchronized with quantum timestamps to ensure spatiotemporal consistency between the holographic projection content and real-time monitoring data. Finally, the report is distributed to various terminal nodes through a quantum-encrypted channel, enabling cross-platform visual collaborative decision-making.

[0121] This embodiment also provides a digital production operation and management system for a mining group, including: an instruction generation module, an anomaly monitoring module, a response control module, an equipment driving module, and a comprehensive evaluation module. The instruction generation module is used by the mining group control center to generate structured control instructions, perform security verification and signature verification through blockchain smart contracts, generate authorized instruction packages, and transmit them to the target mine edge nodes. The anomaly monitoring module is used by the mine edge nodes to receive the authorized instruction packages, collect real-time spatial gravitational changes, calculate the gravitational distortion rate, and input the data into a chaotic synchronization discriminator to generate an anomaly monitoring dataset. The response control module is used to trigger a hierarchical response mechanism based on the gravitational distortion rate sequence in the anomaly monitoring dataset, generating hierarchical control instruction packages. The equipment driving module is used to parse the hierarchical control instruction packages through a spiking neural network, generate a set of safe execution parameters, drive support equipment, ventilation equipment, and power equipment, and simultaneously send collaborative instructions to adjacent mines, generating equipment execution feedback data. The comprehensive evaluation module is used to associate the equipment execution feedback data with the authorized instruction packages, generate a quantum entanglement hash value and verify it, and combine it with satellite remote sensing surface deformation data to generate a dynamic interactive comprehensive management and control report.

[0122] This embodiment also provides a computer device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the digital production operation and management method for mining groups proposed in the above embodiments.

[0123] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0124] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the digital production operation and management method for mining groups as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0125] In summary, this invention generates structured control instructions through the mining group's control center and verifies and signs them using blockchain smart contracts, achieving a high level of security. This not only improves the anti-tampering capability during instruction transmission but also enhances overall security. Based on the anomaly monitoring dataset, a hierarchical response mechanism is triggered to generate hierarchical control instruction packages, which not only improves the early warning capability for potential geological disasters but also enables effective preventive measures to be taken before disasters occur, greatly reducing the probability of accidents.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for digital production operation management and control in a mining group, characterized in that: include: The mining group control center generates structured control instructions, performs security verification and signature verification through blockchain smart contracts, generates authorized instruction packages, and transmits them to the target mine edge nodes. The mine edge node receives authorized command packets, collects real-time spatial gravitational changes and calculates gravitational distortion rate, and simultaneously inputs the data into a chaotic synchronization discriminator to generate an anomaly monitoring dataset. The specific steps are as follows. The edge node of the mine receives the authorized instruction packet, activates the quantum chaotic oscillator, and generates a chaotic carrier signal; The laser phase of the gravity measurement device is modulated using a chaotic carrier signal to collect space gravitational gradient data in real time. Dynamic analysis of time-varying tensor fields is performed on spatial gravitational gradient data to generate spacetime tensor field data and calculate gravitational distortion rate. The gravitational distortion rate is input into the chaos synchronization discriminator, and an anomaly is marked when the degree of chaos desynchronization exceeds the safety threshold in multiple consecutive samplings. The spatiotemporal tensor field data marked with anomalies are compressed and stored to generate an anomaly monitoring dataset; A hierarchical response mechanism is triggered based on the gravitational distortion rate sequence in the anomaly monitoring dataset to generate hierarchical control command packages. The specific steps are as follows. A hypergraph state space is constructed based on the gravitational distortion rate sequence in the anomaly monitoring dataset, and a hypergraph adjacency matrix is ​​generated. A quantum annealing Hamiltonian is constructed using the hypergraph adjacency matrix, and the optimal policy parameter vector is generated through a quantum annealing optimization algorithm. Based on the optimal policy parameter vector and the gravitational distortion rate, the dynamic risk index is calculated by a linear combination of the spatiotemporal weighted integral and the policy weighted rate of change. The response level is determined based on the multi-level risk assessment threshold range in which the dynamic risk index falls, and a graded control instruction package is generated. By parsing the hierarchical control instruction package through a spiking neural network, a set of safe execution parameters is generated, and the support equipment, ventilation equipment and power equipment are driven. At the same time, collaborative instructions are sent to adjacent mines to generate equipment execution feedback data. The device execution feedback data is associated with the authorized instruction package to generate a quantum entanglement hash value and verify it. At the same time, combined with satellite remote sensing surface deformation data, a dynamic interactive comprehensive management and control report is generated.

2. The digital production operation and management method for mining groups as described in claim 1, characterized in that: The mining group's control center generates structured control instructions, which are then used for security verification and signature verification via blockchain smart contracts. The specific steps are as follows: The mining group's control center generates structured control commands through a risk decision engine and inputs them into a quantum random number generator to generate dynamic keys; The structured control commands are encrypted using a dynamic key and the national cryptographic algorithm to form encrypted commands. The encrypted instructions are submitted to the blockchain smart contract for cross-chain security verification and digital signature verification.

3. The digital production operation and management method for mining groups as described in claim 2, characterized in that: The specific steps for generating the authorization instruction packet and transmitting it to the target mine edge node are as follows: Once the security verification is successful, the signed encrypted instructions are combined with the quantum key hash to generate an authorization instruction package; The authorized instruction packet is transmitted to the target mine edge node via a quantum key distribution protocol.

4. The digital production operation and management method for mining groups as described in claim 3, characterized in that: The specific steps for generating a safe execution parameter set by parsing the hierarchical control instruction packet using a spiking neural network are as follows: The hierarchical control instruction package is parsed using the spiking neural network of a neuromorphic chip to generate a device control intent vector; The device control intent vector is input into the digital twin platform for dynamic simulation verification, generating a set of safe execution parameters.

5. The digital production operation and management method for mining groups as described in claim 4, characterized in that: The drive support equipment, ventilation equipment, and power equipment simultaneously send coordinated commands to adjacent mines and generate equipment execution feedback data. The specific steps are as follows. The safety execution parameter set is compiled into a device control signal stream through an FPGA hardware acceleration card, which drives the support equipment, ventilation equipment and power equipment in real time, and collects equipment operation status data in real time. By distributing collaborative instructions to neighboring mines through a service mesh architecture, blockchain-based evidence is generated. By integrating equipment operation status data with blockchain-based evidence, equipment execution feedback data is generated.

6. The digital production operation and management method for mining groups as described in claim 5, characterized in that: The steps for associating device execution feedback data with authorized instruction packets, generating and verifying quantum entanglement hash values, and simultaneously generating a dynamic interactive integrated management and control report by combining satellite remote sensing surface deformation data are as follows: Based on the device execution feedback data and authorization instruction package, a quantum entanglement hash value is generated; Acquire satellite remote sensing data on land surface deformation and generate a land surface deformation field through phase unwrapping and atmospheric correction; The quantum entanglement hash value is spatiotemporally coupled with the surface deformation field for verification, and then verified through a quantum blockchain hybrid consensus mechanism. Based on the verification results, a comprehensive risk index is obtained, and a dynamic, interactive comprehensive management and control report is generated through a holographic rendering engine.

7. A digital production operation management and control system for a mining group, based on the digital production operation management and control method for a mining group as described in any one of claims 1 to 6, characterized in that: It includes an instruction generation module, an anomaly monitoring module, a response control module, a device driver module, and a comprehensive evaluation module. The instruction generation module is used by the mining group control center to generate structured control instructions, perform security verification and signature verification through blockchain smart contracts, generate authorized instruction packages, and transmit them to the target mine edge nodes. The anomaly monitoring module is used by the mine edge node to receive authorized instruction packets, collect spatial gravity changes in real time and calculate the gravitational distortion rate, and input them into the chaos synchronization discriminator to generate anomaly monitoring datasets. The response control module is used to trigger a hierarchical response mechanism based on the gravitational distortion rate sequence in the anomaly monitoring dataset and generate hierarchical control instruction packages. The equipment drive module is used to parse the hierarchical control instruction package through a spiking neural network, generate a set of safe execution parameters, drive the support equipment, ventilation equipment and power equipment, and send collaborative instructions to adjacent mines to generate equipment execution feedback data. The comprehensive evaluation module is used to associate the device's execution feedback data with the authorized instruction package, generate a quantum entanglement hash value and verify it, and at the same time combine satellite remote sensing surface deformation data to generate a dynamic interactive comprehensive management and control report.

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