Shaft Hoisting System and Method Based on Intelligent Control

Abstract

This application relates to the technical field of shaft hoisting system control, and discloses a shaft hoisting system and method based on intelligent control. The system includes: a collection module that collects console signals and the status of the audible and visual signal device to obtain operation instructions and equipment status data; a marking module that adds timestamps and constructs an intrinsically safe distributed control topology; an input module that sends the status data into a sensor network to identify the hoisting status; an implementation module that establishes a dependency graph to implement the horizontal forwarding locking function; a matching module that performs feature extraction and fault identification; and an association module that analyzes the processing strategy and process record and outputs optimized parameter values and locking logic adjustment schemes. This application constructs a distributed control network, combines node-link sensor networks to achieve status analysis, and continuously improves system performance through data-driven fault diagnosis and parameter optimization, thereby significantly improving the safety, reliability, and operating efficiency of the mine hoisting system.

Description

Technical Field

[0001] This application relates to the technical field of shaft hoisting system control, and particularly to a shaft hoisting system and method based on intelligent control. Background Art

[0002] The mine shaft hoisting system is a key equipment in mine production, responsible for the transportation of personnel, materials and equipment between the surface and underground. The traditional mine hoisting signal system mainly adopts a mechanical or simple electrical structure, and realizes the communication between the upper and lower wellheads through mechanical devices such as buttons and pull wires or sound and light signal devices. With the development of technology, some mines have introduced an automated hoisting control system based on PLC, realizing basic signal transmission and safety interlock functions. In the prior art, mine signal transmission equipment such as B66B type equipment can realize an automatic conversion or a moving sidewalk as a life-saving device to replace the normal exit; A62B1 / 02 equipment can cooperate with an air machine or be installed on an aircraft for indicating rescue materials or a device for passengers to escape; B64D9 / 00 equipment is used for lifting or holding the unloading or stopping of loading of aircraft components. These equipment play important roles in their respective fields, providing a basic guarantee for mine safety production.

[0003] However, the existing mine hoisting signal system has many deficiencies. First, the traditional signal system generally lacks intelligent fault diagnosis and processing capabilities. Once a fault occurs, it often requires manual troubleshooting, which is time-consuming, laborious and prone to misjudgment. Second, the signal transmission mechanism of the existing system is relatively rigid and lacks self-adaptive adjustment capabilities, and signal conflicts or losses are likely to occur under complex working conditions. Third, the implementation of the horizontal forwarding locking function is imperfect, and it is unable to effectively handle the signal coordination problem between multiple levels, affecting the hoisting efficiency and safety. Fourth, the optimization of system parameters mainly relies on empirical settings and lacks self-optimization capabilities based on data analysis, making it difficult to adapt to the dynamic changes of the mine environment. Most importantly, the existing systems mostly adopt isolated control methods, making it difficult to achieve collaborative work between control units and unable to form a complete closed-loop intelligent control process, with limited reliability and adaptability in complex mine environments. Summary of the Invention

[0004] This application provides a shaft hoisting system and method based on intelligent control, which is used to build a distributed control network, combine a node link sensor network to realize state analysis, apply a horizontal forwarding locking mechanism to ensure signal transmission safety, and continuously improve system performance through data-driven fault diagnosis and parameter optimization, thereby significantly improving the safety, reliability and operation efficiency of the mine hoisting system.

[0005] In a first aspect, this application provides a shaft hoisting system based on intelligent control, and the shaft hoisting system based on intelligent control includes:

[0006] A collection module, configured to collect the operating console signals and the status information of the audible and visual signal devices at each operating point in the mine shaft hoisting system, so as to obtain hoisting operation instruction data and equipment status data;

[0007] A marking module, configured to perform timestamp marking on the hoisting operation instruction data, and construct a field control network through a controller to obtain an intrinsically safe distributed control topology;

[0008] An input module, configured to input the equipment status data into a hoisting status analysis model based on a node-link perceptron network, identify the hoisting status, and obtain a shaft hoisting control instruction;

[0009] An implementation module, configured to establish a trigger dependency graph among execution units according to the shaft hoisting control instruction, implement a hoisting signal linkage control with a horizontal forwarding locking function, and obtain real-time execution feedback data and a hoisting process record;

[0010] A matching module, configured to perform feature extraction and pattern matching on the real-time execution feedback data, perform abnormal state identification and positioning based on a preset fault feature library, and obtain a hierarchical processing strategy;

[0011] An association module, configured to perform association analysis on the hierarchical processing strategy and the hoisting process record to obtain an optimized value of control parameters and a safety locking logic adjustment scheme.

[0012] In a second aspect, the present application provides a shaft hoisting method based on intelligent control. The shaft hoisting method based on intelligent control includes:

[0013] Collect the operating console signals and the status information of the audible and visual signal devices at each operating point in the mine shaft hoisting system, so as to obtain hoisting operation instruction data and equipment status data;

[0014] Perform timestamp marking on the hoisting operation instruction data, and construct a field control network through a controller to obtain an intrinsically safe distributed control topology;

[0015] Input the equipment status data into a hoisting status analysis model based on a node-link perceptron network, identify the hoisting status, and obtain a shaft hoisting control instruction;

[0016] Establish a trigger dependency graph among execution units according to the shaft hoisting control instruction, implement a hoisting signal linkage control with a horizontal forwarding locking function, and obtain real-time execution feedback data and a hoisting process record;

[0017] Perform feature extraction and pattern matching on the real-time execution feedback data, perform abnormal state identification and positioning based on a preset fault feature library, and obtain a hierarchical processing strategy;

[0018] Perform correlation analysis on the hierarchical processing strategy and the lift process record to obtain an optimized value of the control parameter and an adjustment plan for the safety interlock logic.

[0019] In a third aspect, a shaft hoisting device based on intelligent control is provided, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory to enable the shaft hoisting device based on intelligent control to execute the above-mentioned shaft hoisting method based on intelligent control.

[0020] In a fourth aspect, a computer-readable storage medium is provided, in which instructions are stored, and when it runs on a computer, it enables the computer to execute the above-mentioned shaft hoisting method based on intelligent control.

[0021] In the technical solution provided by this application, by collecting the signal data of each operation point of the mine shaft hoisting system, multi-level collaborative control is realized, significantly improving the safety, reliability and operation efficiency of the hoisting system; timestamp marking is performed on the hoisting operation instruction data and a intrinsically safe distributed control topology structure is constructed, solving the problems of unstable signal transmission and poor adaptability to harsh underground environments in traditional systems, and ensuring precise timing coordination among each execution unit; the lift state analysis model based on the node-link sensor network can accurately identify five lift states: fast up, fast down, slow up, slow down and emergency stop, converting uncertain sensing data into clear control instructions, reflecting the technical contribution of artificial intelligence algorithms in specific application fields. In particular, high-precision state recognition is achieved through a three-layer neural network structure, providing a reliable guarantee for lift control in complex mine environments; the implementation of the dependency graph and the horizontal forwarding interlock function effectively solves the signal conflict problem in the multi-level hoisting system, especially the one-way control logic that only the lower shaft can send signals to the upper shaft, simplifying the system operation mechanism and improving safety at the same time; the feature extraction and fault matching function for real-time execution feedback data realizes early detection and precise positioning of faults, reducing the risk of equipment damage and downtime; the formulation of the hierarchical processing strategy takes both safety and operation efficiency into account, and adopts differential treatment measures according to the fault risk level; the optimized value of the control parameter and the adjustment plan for the safety interlock logic obtained through correlation analysis enable the system to have self-optimization ability, and can continuously adjust working parameters and control logic according to operation data to adapt to the challenges brought by mine environment changes and equipment aging; it is particularly worth emphasizing that the artificial intelligence algorithm in this solution not only improves the accuracy through feature matching in the fault diagnosis link, but also clarifies the improvement direction through sensitivity analysis in the parameter optimization process, reflecting the targeted contribution of the algorithm model in specific application scenarios, transforming traditional experience-based judgment into data-driven decision-making, and significantly improving the intelligent level and operation effect of the mine hoisting system. Description of the Drawings

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a schematic diagram of an embodiment of the shaft hoisting system based on intelligent control in the embodiments of the present application;

[0024] Figure 2 It is a schematic diagram of an embodiment of the shaft hoisting method based on intelligent control in the embodiments of the present application;

[0025] Figure 3 It is a structural schematic block diagram of the shaft hoisting equipment based on intelligent control in the embodiments of the present invention. Specific embodiments

[0026] The embodiments of the present application provide a shaft hoisting system and method based on intelligent control. The terms first, second, third, fourth, etc. (if any) in the specification, claims, and above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the term "including" or "having" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0027] For easy understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 An embodiment of the shaft hoisting system based on intelligent control in the embodiments of the present application includes:

[0028] An acquisition module 101, configured to acquire the operation console signals and the status information of the audible and visual signal devices at each operation point in the mine shaft hoisting system, so as to obtain hoisting operation instruction data and equipment status data;

[0029] A marking module 102, configured to perform timestamp marking on the hoisting operation instruction data, and build a field control network through a controller to obtain an intrinsically safe distributed control topology;

[0030] An input module 103 is configured to input device status data into an enhanced status analysis model based on a node-link perceptron network, identify the enhanced status, and obtain a shaft hoisting control instruction.

[0031] An implementation module 104 is configured to establish a trigger dependency graph among execution units according to the shaft hoisting control instruction, implement a hoisting signal interlock control with a horizontal forwarding lockout function, and obtain real-time execution feedback data and a hoisting process record.

[0032] A matching module 105 is configured to perform feature extraction and pattern matching on the real-time execution feedback data, identify and locate an abnormal status based on a preset fault feature library, and obtain a hierarchical processing strategy.

[0033] An association module 106 is configured to perform an association analysis on the hierarchical processing strategy and the hoisting process record, and obtain an optimized value of a control parameter and a safety lockout logic adjustment scheme.

[0034] It can be understood that the execution entity of this application can be a shaft hoisting system based on intelligent control, or a terminal or a server. Specifically, it is not limited here. In this embodiment of the application, the server is used as an example of the execution entity for illustration.

[0035] In this embodiment of the application, a collection module 101 collects signals of the hoisting system by installing TH12(C) type operating consoles and KXH15(B) type sound and light signal devices at the upper shaft opening, intermediate level, and lower shaft opening. These devices collect raw data through an S7-1200 controller, and then perform Butterworth low-pass filtering on these data. The cut-off frequency is set to 2 Hz to effectively eliminate high-frequency interference generated by underground electrical equipment. The processed signals are encoded according to a signal source identifier, a signal type identifier, a signal value, a timestamp, and a status flag to form a standardized information packet, and then CRC32 checksum is used to ensure data integrity.

[0036] After receiving the standardized information packet, a marking module 102 adds a time mark accurate to the millisecond level to each piece of data using the Precision Time Protocol (PTP). These instruction data with timestamps are then input into a controller network and organized according to a three-layer network architecture, namely, a bottom-layer field layer (execution devices such as operating consoles and signal devices), a middle-layer control layer (S7-1200 and ST40 type controllers), and an upper-layer management layer (KXT131(A)-X type machine room display device). A dual-redundancy communication link is established through PROFIBUS-DP and PROFINET buses, and an improved Raft distributed consensus algorithm is applied to achieve state consistency among nodes. At the same time, the node health status is monitored through a heartbeat mechanism, and the heartbeat period is set to 500 milliseconds. If there is no response for 3 consecutive times, the node is determined to be offline. A intrinsically safe distributed control topology structure that meets the requirements of the "Coal Mine Safety Regulations" is constructed.

[0037] The input module 103 inputs the device status data into the node-link perceptron network for status analysis. First, the network extracts the temporal features of the data, processes the continuous data frames using a 256-point sliding window, and calculates 64-dimensional feature vectors. These feature vectors are input into a three-layer network, including 64 input neurons, 128 hidden-layer neurons (using the ReLU activation function), and 5 output neurons (using the Softmax function), and output five lifting states. In the embodiment of the present application, the five lifting states include the probability distributions of fast upward, fast downward, slow upward, slow upward, and emergency stop. These probability distributions are processed through a Markov decision process model, considering the state transition constraints, and combined with the knowledge graph for matching to generate the shaft lifting control instruction.

[0038] The implementation module 104 parses the control instruction into an execution action sequence and constructs a dependency model in the form of a directed acyclic graph. In view of the characteristics of the mine hoisting system, the horizontal forwarding locking function is implemented to ensure that only the lower shaft can send signals to the upper shaft, and the horizontal locking of this level is completed after the upper shaft receives the signal from the lower shaft. The execution process is divided into three stages: preparation, execution, and completion, and a 5-ms state check period is set. A dual-confirmation mechanism is adopted, requiring two confirmations for instruction reception and execution completion, and a 25-ms confirmation timeout threshold is set. The system collects the response data of the execution unit at a frequency of 10 Hz, records the timestamp, device ID, operation type, and execution result, and stores these data through a three-level database structure (local cache, regional storage, and central warehouse), and sets a 7-day rolling update policy.

[0039] The matching module 105 extracts the mine features from the real-time execution feedback data, and calculates the feedback signal of the sound and light signaler, the status data of the operation console, and the response time of the execution unit. These features are compared with the preset mine fault feature library to identify specific types of faults such as sound and light signal faults, operation console faults, transmission line faults, and controller faults. The system accurately locates the level and device where the fault occurs, evaluates the impact of the fault on the hoisting operation, determines whether it affects signal transmission, blocks horizontal forwarding, or causes the emergency stop signal to be triggered erroneously, and accordingly classifies the faults into three levels: low, medium, and high, and formulates corresponding handling strategies.

[0040] Correlation module 106 performs correlation analysis between the hierarchical processing strategies and the lifting process records. First, a statistical analysis is performed on the fault handling results, calculating the repair time, recovery success rate, and recurrence frequency for different fault types. This data is then time-series correlated with the lifting process records to match the changing trends of system operating parameters before and after the fault, identifying key parameters affecting system stability. A sensitivity analysis is performed to determine the weight of each parameter's impact on system stability and calculate the optimal parameter value. The triggering and release conditions of the lockout event are also analyzed to optimize the signal transmission priority, lockout triggering conditions, and lockout release timing for the upper and lower wellheads and intermediate levels, thereby forming a complete safety lockout logic adjustment plan.

[0041] In a specific embodiment, the acquisition module 101 is configured to:

[0042] An operating table and an acoustic and optical signal device are installed at the upper wellhead, middle level and lower wellhead of the vertical shaft respectively. Signals are collected through the controller to obtain raw data including lifting instructions and equipment status.

[0043] The original data is filtered through a Butterworth low-pass filter to obtain the lifting operation signal;

[0044] Digitally encoding the lifting operation signal according to the signal source identifier, signal type identifier, signal value, time stamp and status flag to obtain a standardized information packet;

[0045] Perform CRC32 checksum and encryption on the standardized information packets, and transmit them to the intelligent processing unit via industrial Ethernet to obtain the system status data.

[0046] A digital twin model of the signal collection points is established based on the hoisting system status data. By updating the status parameters in real time, a virtual mapping structure of the shaft hoisting system is obtained.

[0047] The virtual mapping structure is compared and analyzed with the historical collection data, and abnormal collection points are identified through deviation calculation to obtain improved operation instruction data and equipment status data.

[0048] Specifically, the acquisition module 101 deploys data acquisition devices at key locations in the mine shaft, including the installation of TH12(C) operating consoles and KXH15(B) sound and light signalers at the upper wellhead, middle level, and lower wellhead, respectively. The TH12(C) operating console is an intrinsically safe operating device for mining, with explosion-proof characteristics, capable of stable operation in the harsh, damp and dusty environment underground; while the KXH15(B) sound and light signaler has both sound and light signal prompt functions, making it easy to transmit information in noisy environments and low-light conditions. These devices acquire signals through the S7-1200 programmable controller, which collects data every 100 milliseconds to form a raw data stream. The raw data contains lifting instructions and equipment status.

[0049] The collected raw data is subjected to noise filtering. A large number of electrical devices exist in mine environments, generating electromagnetic interference that can cause signal fluctuations. As a linear filter, the Butterworth low-pass filter has a flat amplitude-frequency characteristic within the passband and a relatively steep cutoff characteristic, making it particularly suitable for processing noise signals in mine environments. In this solution, a fourth-order Butterworth low-pass filter is used with a cutoff frequency set to 2 Hz. This effectively filters out high-frequency noise while retaining the main characteristics of the original signal. The filtered signal is smoother, reducing the probability of false triggering.

[0050] After filtering, the lifting operation signal needs to be standardized and encoded. This standardization process converts the signal into a unified data packet according to a preset format. The data packet contains five key fields: signal source identifier (16 bits, used to indicate the device ID of the data source), signal type identifier (8 bits, distinguishing between lifting instructions and status information), signal value (32 bits, storing the actual data content), timestamp (64 bits, recording the acquisition time with millisecond accuracy), and status flag (8 bits, indicating data validity and processing priority). Standardized encoding enables data from different sources and types to be processed uniformly within the same system.

[0051] Standardized information packets are encrypted and processed using a CRC32 checksum. This CRC32 checksum detects data errors during transmission. It performs polynomial division on each byte in the data stream to generate a 32-bit checksum. The receiving end recalculates and compares the 32-bit checksum using the same algorithm to verify data integrity. Encryption using the AES-128 algorithm protects data from unauthorized access or tampering during transmission over Industrial Ethernet. This checksum-encoded data is transmitted via Industrial Ethernet to the intelligent processing unit, generating system status data.

[0052] To achieve comprehensive monitoring of the shaft hoisting system, a digital twin model of each signal collection point is built based on the collected status data. A digital twin is a virtual replica of a physical entity or system in the digital world, synchronized with the physical entity through real-time data mapping. In this solution, a virtual model is created for each signal collection point (including the operating console and the audible and visual annunciators). The model contains device attributes (model, location, function, etc.) and real-time status parameters. These parameters are updated every second to ensure that the virtual model is synchronized with the actual device status, thus forming a complete virtual mapping structure of the shaft hoisting system.

[0053] The system compares the virtual mapping structure with the historical acquisition data for comparative analysis to identify abnormal acquisition points. The comparative analysis uses statistical methods to calculate the deviation value between the current state and the historical data. First, a benchmark data set in the normal working state is established, including the mean and standard deviation of each parameter. Then, the currently acquired data is compared with the benchmark data to calculate the Z-score (the difference between the current value and the mean divided by the standard deviation). When the Z-score exceeds a preset threshold (usually set at ±3, indicating a deviation of 3 standard deviations), it is determined as an abnormal point. In this way, the system can identify the acquisition points with abnormal data, mark these points, and generate improvement operation instruction data and device status data.

[0054] For example, in a mine shaft hoisting system, TH12(C) type operating consoles and KXH15(B) type audible and visual signal devices are installed at the upper wellhead, middle level, and lower wellhead respectively. When the operator at the upper wellhead presses the slow-down button, the operating console generates an original signal. After the S7-1200 controller collects this signal, it removes interference through a Butterworth low-pass filter and then encodes it into a standardized information packet, which includes the signal source identifier of the upper wellhead operating console 001, the signal type of the hoisting instruction, the signal value of slow-down, the acquisition timestamp, and the normal status flag. The data is verified by CRC32 and encrypted and then transmitted to the intelligent processing unit through the industrial Ethernet. The system then updates the status parameters of the upper wellhead operating console in the digital twin model and conducts comparative analysis with the historical data. When it is found that the response time of a certain audible and visual signal device at the middle level is abnormal (the Z-score is 4.2, exceeding the normal range), the system immediately marks the device as an abnormal point and at the same time transmits the processed slow-down instruction and the device status data containing the abnormal mark to the subsequent processing module.

[0055] In a specific embodiment, the marking module 102 is used for:

[0056] Perform time synchronization marking on the lifting operation instruction data to obtain time-sequenced instruction data;

[0057] Partition the time-sequenced instruction data according to a three-layer network architecture, establish communication nodes through a programmable controller, and obtain a network architecture including the underlying field layer, the middle control layer, and the upper management layer;

[0058] Configure the bus for the communication connections in the network architecture, establish a communication link through dual redundancy design, and obtain a network topology structure;

[0059] Monitor the health status of each node in the network topology structure through a heartbeat mechanism to obtain a distributed decision-making mechanism;

[0060] Logically map the distributed decision-making mechanism and convert it into controller configuration parameters through flameproof, intrinsically safe, and explosion-proof designs to obtain the controller working mode;

[0061] Based on the controller working mode, a node dynamic access rule library is established, and the network topology is adaptively adjusted through the self-organizing network algorithm to obtain an intrinsically safe distributed control topology structure.

[0062] Specifically, the marking module 102 performs time synchronization marking on the hoisting operation instruction data, and this process adopts the Precision Time Protocol (PTP). PTP realizes time synchronization through the master-slave architecture. The master clock device in the system periodically sends synchronization messages to the slave clock devices, and the slave clock devices adjust their clocks according to the received messages and delay calculations. In the shaft hoisting system, the KXT131(A)-X machine room display device serves as the master clock source and sends synchronization messages once every 500 milliseconds. Each operating console and signaler serve as slave clock devices to receive the synchronization signals and adjust their local clocks. The time synchronization error is controlled within 1 millisecond, ensuring that devices at different positions have a unified time reference.

[0063] After the sequential instruction data is generated, it is processed in partitions according to the three-layer network architecture. The three-layer network architecture is a hierarchical network design method, which is specifically divided in the shaft hoisting system into: the bottom field layer, including devices such as the TH12(C) operating console and the KXH15(B) sound and light signaler that directly interact with the physical environment; the middle control layer, composed of S7-1200 and ST40 programmable controllers, responsible for logic control and data processing; the upper management layer, mainly the KXT131(A)-X machine room display device and the monitoring computer, responsible for system monitoring and decision-making. Different layers use different communication protocols and processing priorities. The data acquisition priority of the field layer devices is the highest, the data processing and execution of control logic priority of the control layer is the second, and the monitoring and decision-making priority of the management layer is the lowest. Through this hierarchical processing method, a large number of concurrent instructions can be effectively handled to ensure that critical control instructions are processed in a timely manner.

[0064] After the network architecture is established, the bus configuration of the communication connection is carried out. In the shaft hoisting system, two industrial bus technologies, PROFIBUS-DP and PROFINET, are adopted. PROFIBUS-DP is a high-speed fieldbus applied to the field layer, with a transmission rate of up to 12 Mbps, supporting a multi-master structure, and is suitable for connecting field devices such as operating consoles and signalers; PROFINET is a high-performance bus based on industrial Ethernet, with a transmission rate of up to 100 Mbps, supporting real-time communication, and is mainly used to connect control layer and management layer devices. To improve the system reliability, a dual redundant design is adopted to establish the communication link, that is, each key node has two independent communication paths. When the main path fails, the system automatically switches to the standby path, and the switching time does not exceed 100 milliseconds to ensure that the communication is not interrupted.

[0065] To monitor the operating status of network nodes, a heartbeat mechanism is used to monitor the network topology. The heartbeat mechanism is a simple and effective method for monitoring node status. Its working principle is: each node periodically sends heartbeat messages (containing node ID, timestamp, and status information) to other nodes in the network. If a node does not receive a heartbeat message from another node within a predetermined time, it is considered that the node may have failed. In this system, the heartbeat period is set to 500 milliseconds. If no heartbeat message is received for three consecutive times (i.e., 1.5 seconds), the node is considered offline. Based on the monitoring results of the heartbeat mechanism, the system applies an improved Raft distributed consensus algorithm to achieve state consistency between nodes. The Raft algorithm divides the nodes in the system into three roles: leader, follower, and candidate. It ensures system state consistency through election and log replication mechanisms, and can work normally even when some nodes fail.

[0066] Considering the unique characteristics of mine environments, it's necessary to logically map the distributed decision-making mechanism to the safety design requirements of the Coal Mine Safety Regulations. This process involves three key aspects: flameproof design. Designing equipment enclosures to withstand internal explosions and prevent flame propagation in potentially explosive environments; intrinsically safe design. This ensures that sparks generated by electrical equipment, whether in normal or faulty conditions, are insufficient to ignite surrounding flammable gases; and explosion-proof design. Positive pressure ventilation or sealing are employed to prevent the equipment from becoming an ignition source. These safety design requirements are converted into controller configuration parameters, including current limit (controlled within the intrinsically safe current range, generally less than 100mA), voltage limit (controlled within the intrinsically safe voltage range, typically less than 12V), and communication timeout (set to three times the heartbeat period, i.e., 1.5 seconds). A dynamic node access rule base is established based on the controller's operating mode, and a self-organizing network algorithm is used to achieve adaptive network topology adjustment. This dynamic node access rule base defines the authentication, function identification, and resource allocation rules for new nodes joining the network. Based on edge computing principles, the self-organizing network algorithm allows nodes in the network to autonomously adjust their connections and operating modes based on the current network status and task requirements. When a new console or annunciator is detected, the system automatically completes identity authentication and function mapping, assigns a network address and communication parameters, and establishes a connection with the existing node. Similarly, if a node goes offline, the system re-plans the communication path to ensure network connectivity.

[0067] For example, during the operation of a mine shaft hoisting system, a slow-up instruction is issued from the upper shaft operating platform. This instruction is first added with an accurate timestamp (e.g., 2025-04-21 10:15:32.456). The system distributes and processes the timestamped instruction according to a three-layer architecture: the TH12(C) operating platform at the bottom field layer generates an operation signal; the S7-1200 controller at the middle control layer receives the signal and performs logical processing; the KXT131(A)-X display device at the upper management layer monitors and displays. The instruction is transmitted from the operating platform to the controller through the PROFIBUS-DP bus, and then from the controller to the display device through the PROFINET bus. Each communication link has a backup path to handle main path failures. During this process, the heartbeat mechanism continuously monitors the status of each node. The controller sends a heartbeat message every 500 milliseconds. When the system detects that a signaler at the middle level fails to respond to the heartbeat message three times in a row, it immediately determines that it is offline, applies the Raft algorithm to reallocate control tasks, and adjusts the communication path through the self-organizing network algorithm, transferring the information that originally needed to pass through this signaler to other paths for transmission, ensuring that the instruction can be executed safely and reliably, and at the same time recording and reporting the fault information.

[0068] In a specific embodiment, the input module 103 is used for:

[0069] Extract the time series characteristics of the equipment status data, calculate the change trend of continuous multi-frame data through a sliding window of 256 sampling points and an overlap rate of 75%, and obtain a 64-dimensional equipment status feature vector;

[0070] Input the equipment status feature vector into a node-link perception network with a three-layer structure, where the input layer contains 64 neurons to receive the feature vector, the hidden layer contains 128 neurons with ReLU activation functions, and the output layer contains 5 neurons using the Softmax function corresponding to 5 hoisting states, and calculate the state probability distribution through forward propagation;

[0071] Conduct a Markov decision process analysis based on the state probability distribution to obtain the hoisting state confirmation result;

[0072] Match the hoisting state confirmation result with the hoisting equipment knowledge graph constructed according to the triple structure, calculate the operation decision path through the shortest path algorithm, and obtain the hoisting operation sequence;

[0073] Apply the priority sorting rule to the hoisting operation sequence, and map it through a 3×5-dimensional priority matrix, where the first dimension represents the instruction type, the second dimension represents the hoisting state, and the third dimension is the priority value, to obtain the control instruction set;

[0074] The shaft hoisting control instruction is obtained by encapsulating the control instruction set with a 32-bit execution timing tag, a 64-bit parameter array, and a 16-bit CRC check code.

[0075] Specifically, the input module 103 extracts timing features. A sliding window of 256 sampling points is used to segment the device status data, with an overlap rate of 75% between adjacent windows. This can capture short-term change features while maintaining data continuity. Within each window, time-domain features, frequency-domain features, and statistical features are extracted and combined to form a 64-dimensional device status feature vector. This dimensionality reduction process compresses the original high-dimensional timing data into a feature vector containing key information.

[0076] After the feature vector is extracted, it is input into the node-link perceptron network for state recognition. The node-link perceptron network is a neural network structure designed specifically for the mine hoisting system, consisting of three layers: the input layer contains 64 neurons, corresponding one-to-one to the 64 dimensions of the feature vector; the hidden layer contains 128 neurons, using the ReLU activation function (output equals input when input is greater than 0, otherwise output is 0), which can effectively handle non-linear relationships; the output layer contains 5 neurons, corresponding to five hoisting states: fast up, fast down, slow up, slow down, and emergency stop. The Softmax function is used to convert the output into a probability distribution. The forward propagation process of the network is that data is transformed through the weight matrix and processed by the activation function from the input layer, and then passed to the hidden layer and output layer in sequence. The output of each neuron is used as the input of the next-layer neuron after being transformed by the weight matrix. The 5 neurons in the output layer respectively represent the probabilities of the system being in various hoisting states.

[0077] After obtaining the state probability distribution, the Markov decision process is used for further analysis. The Markov decision process is a stochastic dynamic system model, which is particularly suitable for describing the state transition process of the hoisting system. In this model, the state space S (including 5 hoisting states), the action space A (executable control actions), the state transition probability P (the probability of transitioning from one state to another), and the reward function R (evaluating the quality of states and actions) are defined. The model constructs a 5×5 state transition matrix, which records the transition rules between different hoisting states. For example, the transition probability from slow up to fast up is relatively high, while the transition probability from fast up directly to fast down is relatively low. The posterior probability is calculated through Bayes' formula, and combined with the currently observed state probability distribution and the prior transition probability, the hoisting state confirmation result is obtained.

[0078] After the lifting status is confirmed, it is necessary to match it with the lifting equipment knowledge graph to determine the optimal operation sequence. The knowledge graph is a semantic network structure that stores knowledge in the form of triples of (entity - relationship - entity). In the shaft hoisting system, entities include various types of equipment (operation consoles, signalers, hoisting machines, etc.) and operation states, and relationships include control relationships, dependency relationships, etc. The knowledge graph contains approximately 15 operation nodes, and the Dijkstra shortest path algorithm is used to find the optimal path from the current state to the target state in the graph. The algorithm starts from the starting node, gradually expands to adjacent nodes, calculates the cumulative path weight, and selects the path with the smallest weight as the final decision path. The weight value reflects the complexity, risk level, and time cost of the operation. In this way, the system can find the safest and most efficient operation sequence.

[0079] After the operation sequence is generated, it is necessary to further process it by applying the priority sorting rules. The priority matrix is a 3×5 - dimensional structure. The first dimension represents the instruction type (safety - class, emergency - class, normal - class), the second dimension represents the lifting status (fast up, fast down, slow up, slow down, emergency stop), and the third dimension is the priority value (range 1 - 10, the larger the value, the higher the priority). In the matrix, the priority value of safety - class instructions (such as emergency stop) is set to 8 - 10, the priority value of emergency - class instructions (such as fault handling) is set to 5 - 7, and the priority value of normal - class instructions (such as normal hoisting) is set to 1 - 4. The system determines the priority of each instruction by looking up the table and sorts them from high to low priority to form a set of control instructions with a clear execution order.

[0080] Finally, the set of control instructions is standardized and encapsulated to form executable shaft hoisting control instructions. Three key pieces of information are added during the encapsulation process: a 32 - bit execution timing mark (including 16 - bit absolute time and 16 - bit relative time, used to specify the exact execution time point of the instruction), a 64 - bit parameter array (including specific execution parameters such as speed, displacement, acceleration, etc.), and a 16 - bit CRC check code (generated using polynomial division, used to verify the integrity of the instruction during transmission). The standardized encapsulation ensures that the instructions can be correctly recognized and executed by the execution unit, and at the same time improves the transmission reliability through CRC checking.

[0081] For example, during the operation of a shaft hoisting system in a certain mine, a set of equipment status data is collected through various sensors, including the status of the console buttons, the hoist speed, the position signal, etc. The input module first applies a 256-point sliding window to this set of data for time series feature extraction, and calculates a 64-dimensional feature vector of the equipment operation status. These feature vectors are input into the node-link perceptron network. The 64 neurons in the input layer of the network receive the feature data, and after being transformed by the weight matrix, they are passed to 128 neurons in the hidden layer. After each hidden layer neuron is processed by the ReLU activation function, it is then passed to 5 neurons in the output layer. Finally, the network outputs a set of probability values: [0.05, 0.03, 0.85, 0.06, 0.01], indicating that the probability that the system is currently in the slow upward state is 85%. This probability distribution is combined with the state transition matrix of the Markov decision process for analysis to confirm that the current hoisting state is slow upward. The system then queries the knowledge graph and determines that the shortest path from the slow upward state to the target stop state is: slow upward -> deceleration -> stop, and a total of three operation node instructions need to be executed. These instructions are mapped to priority values through the priority matrix. Among them, the deceleration instruction belongs to the regular type of instruction, and the priority value is 3; the stop instruction also belongs to the regular type of instruction, and the priority value is 4. After the system sorts by priority, it adds an execution time sequence mark, a parameter array, and a CRC check code to each instruction to form a complete control instruction packet, and finally sends it to the execution unit for sequential execution.

[0082] In a specific embodiment, the implementation module 104 is used for:

[0083] Parse the shaft hoisting control instruction into an execution action sequence, and construct a dependency relationship model of the execution units including the upper shaft opening, the middle level, and the lower shaft opening through a directed acyclic graph structure, and obtain the trigger conditions and execution order of each execution unit;

[0084] Apply the horizontal forwarding locking rule to the dependency relationship model, and through the mechanism of locking other signals at the same level until the current signal is confirmed, set the logical relationship that the lower shaft opening can only send signals to the upper shaft opening, and the locking of this level is completed after the upper shaft opening receives the signal sent by the lower shaft opening, so as to obtain a horizontal forwarding locking structure to prevent signal conflicts;

[0085] Implement a hierarchical execution strategy based on the horizontal forwarding locking structure. By dividing the execution process into three stages: preparation, execution, and completion, and setting a status check period for each stage, an execution control process is obtained;

[0086] Add a double confirmation mechanism to the execution control process. Through the two confirmation mechanisms of requiring instruction reception feedback and execution completion feedback, and setting a timeout threshold for each confirmation, an instruction execution guarantee mechanism is obtained;

[0087] Collect the response data of each execution unit based on the instruction execution guarantee mechanism, record the device status, environmental parameters, and operation results during the execution process, and obtain real-time execution feedback data including timestamp, device ID, operation type, and execution result;

[0088] Store the real-time execution feedback data in a three-level database architecture, establish a data backup mechanism through local cache, regional storage, and central warehouse, and set a rolling update policy to obtain an improved process record.

[0089] Specifically, the implementation module 104 parses the shaft hoisting control instructions generated by the input module 103 into a specific execution action sequence. The parsing process is carried out through an instruction decoder, which separates and extracts the execution timing marks, parameter arrays, and check codes in the instruction packet, and verifies the check code to ensure the integrity of the instruction. After the instruction is parsed, a dependency relationship model between execution units is constructed through a directed acyclic graph structure. A directed acyclic graph is a directed graph structure that does not contain loops and is very suitable for representing an execution process with sequential dependencies. In the shaft hoisting system, the nodes of the graph represent execution units at different positions, including the operating platforms and signalers at the upper shaft opening, intermediate level, and lower shaft opening; while the edges of the graph represent the triggering relationships between execution units, specifying the direction and conditions of signal transmission. Analyze the directed acyclic graph through a topological sorting algorithm to determine the triggering conditions and execution order of each execution unit, and form a complete execution plan.

[0090] After the dependency relationship model is established, it is necessary to apply the horizontal forwarding locking rule for constraint. Horizontal forwarding locking is a safety mechanism in the mine hoisting system used to prevent multiple operation points from sending conflicting signals simultaneously. The specific implementation method is: when a signal is sent from an operating platform at a certain level, other operating platforms at the same level will be temporarily locked until the current signal processing is completed; at the same time, signal transmission follows strict directional rules, and the lower shaft opening can only send signals to the upper shaft opening, and cannot send signals to the same level or lower level. This mechanism is implemented through a status lock table, which records the locking status and reasons for each level. When the upper shaft opening receives a signal sent from the lower shaft opening, the system automatically checks and updates the locking status to ensure that the signal is transmitted along the predetermined path and prevent signal conflicts and chaos.

[0091] Based on the horizontal forwarding locking structure, a hierarchical execution strategy is implemented to finely control the execution process. The hierarchical execution strategy divides each execution action into three stages: preparation, execution, and completion. Each stage has clear start and end conditions. The preparation stage mainly conducts resource checks and parameter verification to ensure that the execution environment meets the requirements; the execution stage is the actual execution process of the core operations; the completion stage conducts result confirmation and resource release. A 5-millisecond status check cycle is set for each stage. The system regularly detects the execution status and records the progress. Only when the end condition of a stage is met can it enter the next stage. This hierarchical execution method ensures that complex operations can proceed orderly according to the predetermined steps.

[0092] To further improve the execution reliability, a double confirmation mechanism is added to the execution control flow. Double confirmation is a means to enhance the reliability of information transmission, requiring each execution instruction to obtain confirmations at two key points: instruction reception confirmation and execution completion confirmation. When the execution unit receives an instruction, it first sends a reception confirmation message indicating that the instruction has been correctly received; after the execution is completed, it sends an execution completion confirmation message indicating that the instruction has been successfully executed. To prevent the loss or delay of confirmation messages, the system sets a 25-millisecond timeout threshold for each confirmation. If no confirmation message is received within the timeout period, the retransmission mechanism or the exception handling process will be triggered. This double confirmation mechanism greatly reduces the uncertainty during the instruction execution process and improves the reliability of the system.

[0093] Based on the improved instruction execution guarantee mechanism, the system can comprehensively collect the response data of each execution unit. The data collection is carried out at a frequency of 10 Hz and covers three types of key information: device status information, including device working mode, power status, communication status, etc.; environmental parameter information, including safety-related parameters such as temperature, humidity, gas concentration, etc.; operation result information, including operation type, execution status, completion degree, etc. The collected data is standardized to form a structured data packet containing four core fields: timestamp, device ID, operation type, and execution result, which serves as real-time execution feedback data.

[0094] To ensure secure data storage and efficient access, real-time execution feedback data is stored in a three-tier database architecture. The three-tier database architecture consists of three levels: local cache, regional storage, and central warehouse. The local cache is located near each execution unit and uses in-memory database technology to store high-frequency access data from the last 30 minutes, providing a response speed in milliseconds; the regional storage is located in the control center at each level and uses a relational database to store regional data from the last 24 hours, providing query responses in seconds; the central warehouse is located in the ground control center and uses distributed database technology to store complete historical data and support complex queries and data analysis. The system sets a 7-day rolling update strategy and automatically archives and compresses data that exceeds the retention period, which not only ensures data integrity but also optimizes the use of storage space.

[0095] For example, during a hoisting operation in a mine shaft hoisting system, the input module generated a slow-up control command. The implementation module first parsed the command, extracting the execution time stamp indicating that it should be executed at 10:15:30. The parameter array contained a speed setting of 1.5 m / s², an acceleration of 0.2 m / s², and a target position of 10 meters above ground level. The system then constructed an execution dependency graph, determining the execution sequence as follows: signal from the lower shaft operating console → signal forwarded from the intermediate level → signal confirmed by the upper shaft operating console → hoist execution. Applying the horizontal forwarding blocking rule, when the lower shaft operating console issues a slow-up signal, the other operating consoles on that level are temporarily locked, and the signal is transmitted to the intermediate level and forwarded to the upper shaft operating console. Upon receiving the signal, the upper shaft operating console confirms and releases the lower shaft operating console lock and then issues a control command to the hoist. The entire execution process is divided into three phases: a preparation phase that checks the hoist and safety door status; an execution phase that controls the hoist to move upward at a speed of 1.5 m / s; and a completion phase that decelerates and stops upon reaching the target position. Each execution unit immediately sends a receipt confirmation upon receiving the instruction, and again sends a completion confirmation upon completion. The system collects response data from each execution unit at a frequency of 10 Hz throughout the entire process, recording key parameters such as hoist speed, position, and motor current. This data is stored in real time in a three-level database. Recent data is stored in a local cache, daily queries are performed through regional storage, and long-term data is archived to a central warehouse, forming a complete record of the hoisting process.

[0096] In a specific embodiment, the matching module 105 is configured to:

[0097] Extract mining features from real-time execution feedback data. By calculating the feedback signals from the sound and light signalers, the status data of the operating console, and the response time of the execution unit, the operational feature data of the mine hoisting system is obtained.

[0098] Compare the mine hoisting system operation characteristic data with the preset mining fault characteristic library, and obtain the abnormal signal recognition result by calculating the matching degree with the common mining equipment failure mode;

[0099] Determine the fault category based on the abnormal signal recognition result. By identifying the acoustic-optic signal fault, console fault, transmission line fault, and controller fault, obtain the fault classification of the mine hoisting equipment.

[0100] Locate the position of the fault classification of the mine hoisting equipment. By analyzing the equipment status data of the upper shaft opening, intermediate level, and lower shaft opening, obtain the fault area and equipment identification.

[0101] Evaluate the impact degree on the hoisting operation based on the fault area and equipment identification. By judging whether it affects the hoisting signal transmission, whether it blocks the horizontal forwarding function, and whether it causes the emergency stop signal to be triggered erroneously, classify the faults into three levels: low, medium, and high, and obtain the fault risk rating.

[0102] Formulate a processing strategy according to the fault risk rating, including maintaining operation and planning maintenance for low-level faults, repairing after completing the current hoisting cycle for medium-level faults, and immediately shutting down the machine for high-level faults, and obtain the hierarchical processing strategy.

[0103] Specifically, the matching module 105 extracts mine characteristics from the real-time execution feedback data. The feature extraction process focuses on analyzing three types of key data according to the characteristics of the shaft hoisting system: the feedback signal of the acoustic-optic signaler, including parameters such as the sound output state, the flashing frequency of the optical signal, and the brightness change; the console status data, including information such as the button trigger state, the working state of the display module, and the power supply voltage; the response time of the execution unit, recording the delay time from the instruction issuance to the response of the execution unit. The extraction method uses the sliding time window technique, with a 10-second window and a 5-second slide each time, and calculates the time series statistical features within the window, including the average value, standard deviation, maximum value, minimum value, and change rate. For the acoustic-optic signaler, additionally calculate the periodic feature of the signal to detect whether there is an abnormal signal interruption or frequency offset; for the console, focus on analyzing the time correlation between button trigger and display response; for the execution unit, compare the response time with the preset normal response time range to identify abnormal delays. Through these feature extractions and calculations, form an operation feature dataset of the mine hoisting system, including the working state features and performance indicators of each component of the system.

[0104] After the feature data is extracted, it is compared with a pre-set mine fault feature library. The mine fault feature library is a reference database specifically designed for the mine hoisting system, containing feature descriptions of various common faults. The fault modes in the library are classified according to equipment types and fault natures, covering various types such as acoustic and optical signaler faults (e.g., sound failure, abnormal flashing of optical signals), operation console faults (e.g., button jamming, abnormal display), transmission line faults (e.g., signal interruption, severe interference), and controller faults (e.g., abnormal program, memory overflow). In the comparison process, similarity calculation methods are used to calculate metrics such as Euclidean distance and cosine similarity between the extracted operation feature data and the feature vectors of each fault mode in the library, generating a matching degree score. The Euclidean distance calculates the straight-line distance between feature vectors, while the cosine similarity focuses on the direction consistency of feature vectors. The system sets a similarity threshold. When the matching degree exceeds the threshold, it is determined that an anomaly is detected, and the anomaly signal recognition result is output, including the possible fault type and the matching degree score.

[0105] Based on the anomaly signal recognition result, the system further determines the specific fault category. The determination process uses a decision tree algorithm to construct a mapping relationship from anomaly features to specific fault types. Each node of the decision tree represents a feature judgment condition, and each path corresponds to a fault diagnosis logic. For example, when it is detected that the optical signal intensity of the acoustic and optical signaler is abnormally low, but the sound output is normal, and the power supply voltage is within the normal range, it is determined that the light source of the acoustic and optical signaler is faulty. Through layer-by-layer screening, the decision tree finally classifies the faults into four categories: acoustic and optical signal faults, operation console faults, transmission line faults, and controller faults. Each category of faults is further divided into multiple specific sub-categories. For example, acoustic and optical signal faults are divided into sound module faults, light source faults, power supply faults, etc.; operation console faults are divided into button faults, display module faults, communication module faults, etc. Through this detailed classification, the system can accurately identify the specific type of the fault, providing a clear direction for subsequent processing. After the fault category is determined, precise location is required to clarify the specific area and equipment where the fault occurs. The location process is achieved by analyzing the equipment status data from different areas. The system compares and analyzes the equipment status data of the upper shaft, intermediate level, and lower shaft to find the source of the abnormal data. The analysis methods include signal propagation path tracking and abnormal feature correlation analysis. Signal propagation path tracking is to follow the signal transmission direction, check the status data of each node, and find the node where the anomaly first appears; abnormal feature correlation analysis is to find the correlation of abnormal features between equipment in different areas. If the equipment in multiple areas shows similar anomalies, it may be caused by a common upstream fault. Through these analyses, the system can determine the location of the fault, accurate to the specific level and equipment, and output a location result containing the fault area coordinates and the unique identifier of the equipment.

[0106] After fault location is completed, the system evaluates the impact of the fault on the lifting operation based on the fault area and equipment identification. Three key factors are considered in the evaluation process: whether it affects the lifting signal transmission, by checking whether the faulty equipment is located on the critical signal transmission path and whether the nature of the fault will cause signal loss or error; whether it blocks the horizontal forwarding function, by determining whether the fault will interrupt the signal forwarding between different levels and affect the coordinated operation of the entire lifting system; whether it causes the emergency stop signal to be triggered erroneously, by judging whether the fault may cause the system to trigger the emergency stop mechanism erroneously and result in unnecessary shutdown. Based on the comprehensive evaluation of these three factors, the system classifies the faults into three levels: low-level faults, which have a slight impact on the lifting operation and do not cause signal transmission problems or horizontal forwarding function malfunctions; medium-level faults, which will partially affect the lifting operation, such as causing some signal transmission delays or instabilities, but will not completely interrupt the operation; high-level faults, which seriously affect the lifting operation, such as blocking the critical signal transmission path or causing the emergency stop signal to be triggered erroneously. The evaluation result is output in the form of a fault risk rating, providing a decision-making basis for subsequent processing.

[0107] Finally, the system formulates corresponding handling strategies according to the fault risk rating. The handling strategies follow the principle of putting safety first and taking efficiency into account, and formulate differentiated handling plans for faults of different risk levels: for low-level faults, adopt the strategy of maintaining operation and scheduling maintenance, the system continues to operate normally, and at the same time arranges equipment maintenance in the next maintenance cycle; for medium-level faults, adopt the strategy of repairing after completing the current lifting cycle, allowing the system to complete the ongoing lifting operation, but requiring immediate repair after the end of the current cycle; for high-level faults, adopt the strategy of immediate shutdown and handling, the system triggers the safety shutdown procedure, interrupts the current lifting operation, and initiates the emergency repair process. The handling strategies also include specific implementation details, such as maintenance personnel scheduling, spare parts preparation, alternative path configuration, etc., to ensure that the faults can be handled in a timely and effective manner. The final output hierarchical handling strategy includes information such as handling method, execution time, resource requirements, and expected recovery time, providing clear guidance for system operation and maintenance.

[0108] For example, during the operation of a mine shaft hoisting system, the matching module collected abnormal feedback data from the KXH15(B) type acoustic-optic signaler at the intermediate level, manifested as unstable flashing frequency of the optical signal and extended response time. The system first extracted the operating characteristic data of the acoustic-optic signaler, including the change in the flashing frequency of the optical signal (varying from the normal 1 time / second to 0.7 - 1.3 times / second), the response time (extended from the normal 50 milliseconds to 120 milliseconds), and the power supply voltage (remaining within the normal range of 12V ± 0.5V). Comparing these characteristic data with the mine use fault characteristic library, it was found that the matching degree with the fault mode of the acoustic-optic signaler drive circuit reached 0.82, exceeding the threshold of 0.75, so it was determined that there was an abnormality. Through decision tree analysis, the system confirmed that this was a typical acoustic-optic signal fault, and the specific subclass was the fault of the flashing control circuit. The location positioning showed that the fault occurred on the 3rd acoustic-optic signaler at the intermediate level. By analyzing the position of this device in the horizontal forwarding chain, it was determined that it was responsible for forwarding the signals from the lower wellhead to the upper wellhead. Based on this positioning result, the system evaluated that this fault would partially affect the hoisting signal transmission, but since the system was designed with redundant transmission paths, it would not completely block the horizontal forwarding function and would not cause false triggering of the emergency stop signal, so it was classified as a medium-level risk. According to the processing strategy for medium-level faults, the system decided to allow the current ongoing hoisting operation to be completed, but after the end of this hoisting cycle, immediately arrange maintenance personnel to replace the flashing control circuit board of the acoustic-optic signaler, and temporarily enable the backup signal transmission path to ensure that the system can still operate normally during the maintenance period.

[0109] In a specific embodiment, the association module 106 is used for:

[0110] Statistically analyze the fault handling effects in the hierarchical processing strategy, and obtain the fault handling effect evaluation data by calculating the repair time, recovery success rate, and recurrence frequency of different fault types;

[0111] Perform time series association between the fault handling effect evaluation data and the hoisting process records, and obtain the key parameter list by matching the change trends of the system operation parameters before and after the fault occurs;

[0112] Conduct sensitivity analysis on the key parameter list, and determine the influence weight of each parameter on the system stability through single-factor change tests to obtain the parameter influence degree ranking result;

[0113] Based on the parameter influence degree ranking result, perform adjustment calculations on the control parameters, and determine the optimal value range of each parameter by setting the safety margin coefficient to obtain the optimized control parameter values;

[0114] Conduct logical analysis on the locking events in the hoisting process records, and obtain the optimization direction of the locking logic by extracting the association rules of the locking trigger conditions and locking release conditions;

[0115] Design a safety interlock logic adjustment scheme based on the optimization direction of the interlock logic, and configure the signal transmission priorities, interlock trigger conditions, and interlock release timings for the upper and lower wellheads and the intermediate level to obtain the safety interlock logic adjustment scheme.

[0116] Specifically, the correlation module 106 conducts a comprehensive statistical analysis of the fault handling effects in the hierarchical processing strategy generated by the matching module. The statistical analysis process focuses on three core indicators: repair time, which records the time length from fault discovery to complete repair, and is subdivided into three sub - stages: fault confirmation time, maintenance preparation time, and actual repair time; recovery success rate, which calculates the proportion of the equipment operating normally after fault repair, and determines whether the repair is successful by analyzing the operating data within one week after repair; recurrence frequency, which counts the number of times the same equipment or the same type of fault appears repeatedly within a specific time window. During the analysis, it is classified according to fault types (acoustic - optical signal faults, console faults, transmission line faults, and controller faults) and fault levels (low - level, medium - level, high - level). The sliding time - window technique is adopted, with a one - month window that slides once a week, to calculate the time trends of various indicators. Through these statistical analyses, an evaluation data table containing the handling effects of various faults is generated, reflecting the handling efficiency and long - term reliability of different types of faults.

[0117] After the generation of fault handling effect evaluation data, it is necessary to perform time series correlation analysis with the improvement process records to find the potential correlation between faults and system operation parameters. Time series correlation is a data mining technique used to discover the correlation and causal relationship between different time series data. The specific implementation method is to mark the fault events on the time axis, and then analyze the change trend of system operation parameters within a certain time window before and after the fault (usually 24 hours before the fault to 72 hours after the fault). The analysis process uses two methods: lag correlation analysis and Granger causality test. Lag correlation analysis calculates the correlation coefficient between parameters and faults at different time delays to find the time delay with the maximum correlation; the Granger causality test judges whether the historical data of a certain parameter can significantly improve the prediction ability of fault occurrence by establishing an autoregressive model. Through these analyses, the system screens out the system operation parameters highly correlated with the fault to form a list of key parameters, which usually includes various parameters such as communication delay time, signal strength fluctuation, power supply voltage stability, environmental temperature and humidity, etc. After the key parameters are determined, sensitivity analysis is carried out on them to quantitatively evaluate the influence degree of each parameter on system stability. Sensitivity analysis uses the single-factor change test method, that is, under the condition of keeping other parameters unchanged, the target parameter is slightly adjusted, and the change of system stability index is observed. The specific operation is to increase or decrease each key parameter by 5% step in its normal value range in the test environment, and record the change value of the system stability index after each adjustment. The system stability indexes include signal transmission success rate, instruction execution accuracy rate, system response time, etc. By calculating the change amount of the stability index caused by each unit parameter change, the influence sensitivity of each parameter is obtained. The higher the sensitivity value, the greater the influence of the parameter on system stability. Finally, the parameters are sorted from high to low according to the sensitivity to generate the parameter influence degree sorting result, providing a basis for subsequent parameter optimization.

[0118] Based on the sorting results of parameter influence degrees, the system adjusts and calculates the control parameters to determine the optimal range of parameter values. The core of the adjustment calculation is to maximize the system performance while ensuring the system safety. The concept of a safety margin coefficient is introduced in the calculation process, that is, on the basis of the theoretical optimal value, a certain margin is reserved to ensure safety. For parameters of different importance levels, different safety margin coefficients are set: the safety margin coefficient of key safety parameters is relatively large, usually 1.5 - 2.0; the safety margin coefficient of general performance parameters is relatively small, usually 1.1 - 1.3. The specific adjustment method is as follows: First, determine the theoretical optimal value of each parameter according to the sensitivity analysis results, and then multiply it by the corresponding safety margin coefficient to obtain the recommended value for actual application. For example, if the theoretical optimal timeout for a certain communication parameter is 20 milliseconds and the safety margin coefficient is 1.5, the actually set timeout should be 30 milliseconds. In this way, the system generates a control parameter optimization value table containing the optimal range of each control parameter to guide subsequent system configuration adjustments.

[0119] In addition to parameter optimization, the system also needs to conduct a special logical analysis of the locking events in the lifting process records to explore optimization opportunities in the locking mechanism. The locking event analysis first extracts all locking-related events from the lifting process records, including locking trigger events and locking release events. Then, perform temporal analysis and pattern mining on these events to identify common trigger-release sequences and abnormal situations. Temporal analysis evaluates the efficiency of the current locking logic by calculating indicators such as the locking state duration and locking frequency; pattern mining uses association rule learning algorithms to discover the regular associations between locking trigger conditions and release conditions. Through these analyses, the system can identify problem points in the current locking logic, such as unnecessary locking triggers, overly long locking durations, and overly complex locking release conditions, thereby determining the optimization direction of the locking logic and providing a clear goal for subsequent adjustments.

[0120] Based on the optimization direction of the locking logic, a detailed safety locking logic adjustment plan is designed. The adjustment plan focuses on three aspects: signal transmission priority configuration, which assigns reasonable priorities to signals of different levels and types to ensure that critical signals can be transmitted first; locking trigger condition setting, which simplifies and refines the locking trigger conditions to reduce the probability of false triggering and missed triggering; and locking release timing optimization, which clearly defines the conditions and processes for unlocking and shortens the unnecessary locking duration. In specific implementation, first, basic priorities are set for various signals at the upper shaft opening, intermediate level, and lower shaft opening (such as emergency signal 10, regular hoisting signal 7, status query signal 4), and then they are dynamically adjusted according to the current system operating state (such as when the hoisting operation is in progress, the priority of relevant signals is increased by 2 points). The locking trigger condition adopts a three-stage design: precondition (the system is in a specific state), trigger event (a specific signal is sent or a specific operation is executed), and confirmation condition (the state change of relevant equipment or the receipt of feedback signals). Only when all three conditions are met simultaneously can the locking be triggered. The unlocking also adopts a combination of multiple conditions, but an automatic timeout unlocking mechanism is added. Even if some conditions are not met, the locking is automatically released after exceeding the preset maximum locking time to prevent the system from being locked for a long time.

[0121] The above describes the shaft hoisting system based on intelligent control in the embodiments of the present application. Next, the shaft hoisting system based on intelligent control in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the shaft hoisting system based on intelligent control in the embodiments of the present application includes:

[0122] S201. Collect the console signals and the status information of the audible and visual signal devices at each operation point in the mine shaft hoisting system to obtain hoisting operation instruction data and equipment status data;

[0123] S202. Mark the time stamps for the hoisting operation instruction data, and build a field control network through the controller to obtain an intrinsically safe distributed control topology;

[0124] S203. Input the equipment status data into the hoisting status analysis model based on the node-link perception network to identify the hoisting status and obtain the shaft hoisting control instruction;

[0125] S204. Establish a trigger dependency graph among the execution units according to the shaft hoisting control instruction, and implement the hoisting signal linkage control with the horizontal forwarding locking function to obtain the real-time execution feedback data and the hoisting process record;

[0126] S205. Extract the features and perform pattern matching on the real-time execution feedback data, and identify and locate the abnormal state based on the preset fault feature library to obtain the hierarchical processing strategy;

[0127] S206. Perform correlation analysis on the grading processing strategy and the lifting process record to obtain the optimized control parameter value and the safety interlock logic adjustment plan.

[0128] In this application, by collecting the signal data of each operation point of the mine shaft hoisting system, multi-level collaborative control is achieved, significantly improving the safety, reliability and operation efficiency of the hoisting system; timestamp marking is performed on the hoisting operation instruction data and a intrinsically safe distributed control topology structure is constructed, solving the problems of unstable signal transmission and poor adaptability to the harsh underground environment in the traditional system, and ensuring the precise timing coordination among the execution units; the hoisting state analysis model based on the node-link perceptron network can accurately identify five hoisting states, namely fast upward, fast downward, slow upward, slow downward and emergency stop, converting the uncertain sensing data into clear control instructions, reflecting the technical contribution of the artificial intelligence algorithm in a specific application field. In particular, high-precision state recognition is achieved through a three-layer neural network structure, providing a reliable guarantee for hoisting control in a complex mine environment; the implementation of the dependency graph and the horizontal forwarding interlock function effectively solves the signal conflict problem in the multi-level hoisting system, especially the one-way control logic that only the lower wellhead can send signals to the upper wellhead, simplifying the system operation mechanism and improving the safety at the same time; the feature extraction and fault matching function for the real-time execution feedback data realizes the early detection and precise positioning of faults, reducing the risk of equipment damage and downtime; the formulation of the grading processing strategy takes both safety and operation efficiency into account, and differential treatment measures are taken according to the fault risk level; the optimized control parameter value and the safety interlock logic adjustment plan obtained through correlation analysis enable the system to have self-optimization ability, and can continuously adjust the working parameters and control logic according to the operation data to adapt to the challenges brought by the change of the mine environment and the aging of equipment; it is particularly worth emphasizing that the artificial intelligence algorithm in this solution not only improves the accuracy through feature matching in the fault diagnosis link, but also clarifies the improvement direction through sensitivity analysis in the parameter optimization process, reflecting the targeted contribution of the algorithm model in a specific application scenario, transforming the traditional experience judgment into data-driven decision-making, and significantly improving the intelligent level and operation effect of the mine hoisting system.

[0129] Above Figure 2 The shaft hoisting system based on intelligent control in the embodiment of the present invention is described in detail from the perspective of modular functional entities. Next, the shaft hoisting equipment based on intelligent control in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0130] Figure 3FIG. 0 is a schematic structural diagram of a shaft hoisting device based on intelligent control provided by an embodiment of the present invention. The shaft hoisting device 300 based on intelligent control may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 for storing application programs 333 or data 332 (for example, one or more mass storage device terminals). Among them, the memory 320 and the storage media 330 may be transient storage or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the shaft hoisting device 300 based on intelligent control. Further, the processor 310 may be configured to communicate with the storage media 330 and execute a series of instruction operations in the storage media 330 on the shaft hoisting device 300 based on intelligent control to implement the steps of the above-mentioned shaft hoisting system based on intelligent control.

[0131] The shaft hoisting device 300 based on intelligent control may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, and so on. Those skilled in the art can understand that Figure 3 The shown structural diagram of the shaft hoisting device based on intelligent control does not limit the shaft hoisting device based on intelligent control provided by the present invention, and may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.

[0132] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is made to execute the steps of the shaft hoisting system based on intelligent control.

[0133] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system, system, and unit can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0134] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable an intelligent control-based shaft hoisting device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0135] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A shaft hoisting system based on intelligent control, characterized in that, The system includes: A collection module, which is used to collect the operation console signals and the status information of the audible and visual signal devices at each operation point in the mine shaft hoisting system, and obtain hoisting operation instruction data and equipment status data; A marking module, which is used to perform timestamp marking on the hoisting operation instruction data, and construct a field control network through a controller to obtain an intrinsically safe distributed control topology; An input module, which is used to input the equipment status data into a hoisting status analysis model based on a node-link perceptron network, identify the hoisting status, and obtain a shaft hoisting control instruction; An implementation module, which is used to establish a trigger dependency graph among execution units according to the shaft hoisting control instruction, implement a hoisting signal linkage control with a horizontal forwarding locking function, and obtain real-time execution feedback data and a hoisting process record; A matching module, which is used to extract features and perform pattern matching on the real-time execution feedback data, identify and locate abnormal states based on a preset fault feature library, and obtain a hierarchical processing strategy; An association module, which is used to perform association analysis on the hierarchical processing strategy and the hoisting process record, and obtain an optimized control parameter value and a safety locking logic adjustment plan.

2. The shaft hoisting system based on intelligent control according to claim 1, wherein, The collection module is used for: Install operation consoles and type audible and visual signal devices at the upper shaft opening, intermediate level, and lower shaft opening respectively, and collect signals through a controller to obtain raw data containing hoisting instructions and equipment status; Filter the noise of the raw data through a Butterworth low-pass filter to obtain a hoisting operation signal; Digitally encode the hoisting operation signal according to the signal source identifier, signal type identifier, signal value, timestamp, and status flag to obtain a standardized information packet; Perform CRC32 checksum and encryption processing on the standardized information packet, and transmit it to the intelligent processing unit through industrial Ethernet to obtain hoisting system status data; Establish a digital twin model of the signal collection point according to the hoisting system status data, and obtain a virtual mapping structure of the shaft hoisting system by updating the status parameters in real time; Compare and analyze the virtual mapping structure with historical collection data, identify abnormal collection points through deviation calculation, and obtain hoisting operation instruction data and equipment status data.

3. The shaft hoisting system based on intelligent control according to claim 1, wherein The marking module is used for: Perform time synchronization marking on the hoisting operation instruction data to obtain sequential instruction data; Partition the sequential instruction data according to a three-layer network architecture, and establish communication nodes through a programmable controller to obtain a network architecture including a bottom field layer, a middle control layer, and an upper management layer; Configure the bus for the communication connections in the network architecture, and establish a communication link through dual redundant design to obtain a network topology; Monitor the health status of each node of the network topology through a heartbeat mechanism to obtain a distributed decision-making mechanism; Logically map the distributed decision-making mechanism, and convert it into controller configuration parameters through flameproof, intrinsically safe, and explosion-proof designs to obtain a controller working mode; Based on the controller working mode, establish a node dynamic access rule library, and realize the adaptive adjustment of the network topology through a self-organizing network algorithm to obtain an intrinsically safe distributed control topology.

4. The shaft hoisting system based on intelligent control according to claim 1, characterized in that, The input module is used for: Extract the temporal features of the device status data, calculate the change trend of consecutive multi-frame data through a sliding window of 256 sampling points and an overlap rate of 75%, and obtain a 64-dimensional device status feature vector; Input the device status feature vector into a node-link perceptron network with a three-layer structure, where the input layer contains 64 neurons to receive the feature vector, the hidden layer contains 128 neurons with ReLU activation functions, and the output layer contains 5 neurons using the Softmax function corresponding to 5 lifting states, and calculate the state probability distribution through forward propagation; Perform Markov decision process analysis based on the state probability distribution to obtain the result of confirming the lifting state; Match the result of confirming the lifting state with the knowledge graph of the lifting device constructed according to the triple structure, calculate the operation decision path through the shortest path algorithm, and obtain the lifting operation sequence; Apply the priority sorting rule to the lifting operation sequence, and map it through a 3×5-dimensional priority matrix, where the first dimension represents the instruction type, the second dimension represents the lifting state, and the third dimension is the priority value, to obtain a set of control instructions; Complete the instruction encapsulation by adding a 32-bit execution timing mark, a 64-bit parameter array, and a 16-bit CRC check code to the set of control instructions to obtain a shaft lifting control instruction.

5. The shaft hoisting system based on intelligent control according to claim 1, characterized in that Implementation module, for: Parse the shaft lifting control instruction into an execution action sequence, construct a dependency relationship model of the execution units including the upper wellhead, middle level, and lower wellhead with a directed acyclic graph structure, and obtain the trigger conditions and execution order of each execution unit; Apply the horizontal forwarding locking rule to the dependency relationship model, set the logical relationship that the lower wellhead can only send signals to the upper wellhead and the locking of this level is completed after the upper wellhead receives the signal from the lower wellhead through the mechanism of locking other signals at the same level until the current signal is confirmed, to obtain a horizontal forwarding locking structure to prevent signal conflicts; Implement a hierarchical execution strategy based on the horizontal forwarding locking structure, divide the execution process into three stages: preparation, execution, and completion, and set a status check period for each stage to obtain an execution control process; Add a double confirmation mechanism to the execution control process, require two confirmation mechanisms of instruction reception feedback and execution completion feedback, and set a timeout threshold for each confirmation to obtain an instruction execution guarantee mechanism; Collect the response data of each execution unit based on the instruction execution guarantee mechanism, record the device status, environmental parameters, and operation results during the execution process, and obtain real-time execution feedback data including timestamp, device ID, operation type, and execution result; Store the real-time execution feedback data in a three-level database architecture, establish a data backup mechanism through local cache, regional storage, and central warehouse, and set a rolling update strategy to obtain a record of the lifting process.

6. The shaft hoisting system based on intelligent control according to claim 1, characterized in that, Matching module, for: Extract the mine use features from the real-time execution feedback data, calculate the feedback signal of the sound and light signaler, the status data of the operation console, and the response time of the execution unit, and obtain the operation feature data of the mine hoisting system; Compare the operation characteristic data of the mine hoisting system with a preset mine fault feature library, and obtain the abnormal signal recognition result by calculating the matching degree with common mine equipment fault modes; Determine the fault category based on the abnormal signal recognition result, and obtain the fault classification of the mine hoisting equipment by identifying the sound and light signal fault, the operation console fault, the transmission line fault, and the controller fault; Locate the position of the fault classification of the mine hoisting equipment, and obtain the fault area and equipment identification by analyzing the equipment status data of the upper wellhead, the intermediate level, and the lower wellhead; Evaluate the impact degree on the hoisting operation based on the fault area and equipment identification. By judging whether it affects the hoisting signal transmission, whether it blocks the horizontal forwarding function, and whether it causes the emergency stop signal to be accidentally triggered, classify the faults into three levels: low, medium, and high, and obtain the fault risk rating; Formulate a processing strategy according to the fault risk rating, including maintaining operation and planning maintenance for low-level faults, repairing after completing the current hoisting cycle for medium-level faults, and immediately shutting down and handling high-level faults, to obtain a hierarchical processing strategy.

7. The shaft hoisting system based on intelligent control according to claim 1, characterized in that, The association module is used for: Statistically analyze the fault handling effects in the hierarchical processing strategy, and obtain the fault handling effect evaluation data by calculating the repair time, recovery success rate, and recurrence frequency of different fault types; Perform time series association on the fault handling effect evaluation data and the hoisting process record, and obtain the key parameter list by matching the change trend of the system operation parameters before and after the fault occurs; Conduct sensitivity analysis on the key parameter list, and obtain the parameter influence degree ranking result by determining the influence weight of each parameter on the system stability through single-factor change tests; Adjust and calculate the control parameters based on the parameter influence degree ranking result, and obtain the optimized control parameter value by setting the safety margin coefficient to determine the optimal value range of each parameter; Logically analyze the locking events in the hoisting process record, and obtain the optimization direction of the locking logic by extracting the association rules of the locking trigger condition and the locking release condition; Design a safety locking logic adjustment plan based on the optimization direction of the locking logic, and configure the signal transmission priority, locking trigger condition, and locking release timing sequence of the upper and lower wellheads and the intermediate level to obtain a safety locking logic adjustment plan.

8. A shaft hoisting method based on intelligent control, characterized in that, The shaft hoisting method based on intelligent control includes: Collect the operation console signals and the status information of the sound and light signal devices at each operation point in the mine shaft hoisting system, and obtain the hoisting operation instruction data and the equipment status data; Mark the time stamp for the hoisting operation instruction data, and construct a field control network through the controller to obtain an intrinsically safe distributed control topology; Input the equipment status data into the hoisting status analysis model based on the node-link perceptron network to identify the hoisting status and obtain the shaft hoisting control instruction; Establish a trigger dependency graph between the execution units according to the shaft hoisting control instruction, and implement the hoisting signal linkage control with a horizontal forwarding locking function to obtain the real-time execution feedback data and the hoisting process record; Extract features and perform pattern matching on the real-time execution feedback data, identify and locate abnormal states based on a pre-set fault feature library, and obtain a hierarchical processing strategy; Perform correlation analysis on the hierarchical processing strategy and the improvement process record to obtain an optimized value of the control parameter and an adjustment plan for the safety interlock logic.

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