Mine equipment life prediction and maintenance system based on digital twinning

Abstract

The invention discloses a mine equipment life prediction and maintenance system based on digital twinning, and belongs to the technical field of mine equipment operation and maintenance management. The system comprises a data acquisition end used for acquiring operation data of mine equipment, acquiring environmental parameters of an environment where the mine equipment is located, and uploading component operation parameters, operation states and the environmental parameters to an operation and maintenance processing end, the operation data comprises component operation parameters of each component in the mine equipment and an operation state of the mine equipment; the operation and maintenance processing end is used for simulating the current operation condition of the mine equipment through a preset mine equipment and environment joint digital twinborn model according to the operation parameters, the operation state and the environment parameters of the components, and predicting the residual life of the mine equipment; and the operation and maintenance processing end is also used for adaptively generating a corresponding operation and maintenance strategy according to the current operation condition and the residual life, and sending the operation and maintenance strategy to related personnel to prompt the related personnel to carry out corresponding operation and maintenance work.

Description

Technical Field

[0001] This application relates to the field of mine equipment operation and maintenance management technology, and in particular to a mine equipment life prediction and maintenance system based on digital twins. Background Technology

[0002] Mining environments are typically characterized by high corrosivity and strong disturbance. Equipment operates under harsh conditions of high temperature, high humidity, and high dust concentration for extended periods, and its lifespan decline is essentially the result of the combined effects of mechanical stress fatigue and environmental erosion.

[0003] However, traditional maintenance systems have fundamental limitations: they treat environmental variables such as temperature and dust as background parameters independent of equipment operation, ignoring their dynamic coupling effect with mechanical wear, making it impossible to accurately predict the remaining lifespan of mining equipment. Furthermore, early warning mechanisms based on fixed thresholds cannot capture the hidden degradation trajectory of equipment, thus making it impossible to accurately arrange operation and maintenance plans that adapt to changes in equipment lifespan.

[0004] Therefore, there is a significant problem in the current operation and maintenance management of mining equipment: the inability to accurately predict the remaining lifespan of mining equipment, which in turn makes it impossible to formulate reasonable operation and maintenance plans for mining equipment. Summary of the Invention

[0005] The main purpose of this application is to provide a mining equipment life prediction and maintenance system based on digital twins, which aims to solve the technical problem that the remaining life of mining equipment cannot be accurately predicted, and therefore it is impossible to formulate reasonable operation and maintenance plans for mining equipment.

[0006] To achieve the above objectives, this application provides a digital twin-based system for predicting and maintaining the lifespan of mining equipment, which includes a data acquisition terminal and an operation and maintenance processing terminal. The data acquisition terminal is used to acquire the operating data of the mining equipment and the environmental parameters of the environment in which the mining equipment is located, and to upload the component operating parameters, the operating status and the environmental parameters to the operation and maintenance processing terminal. The operating data includes the component operating parameters of each component in the mining equipment and the operating status of the mining equipment. The operation and maintenance processing terminal is used to simulate the current operating status of the mine equipment and predict the remaining lifespan of the mine equipment based on the component operating parameters, the operating status and the environmental parameters through a preset joint digital twin model of mine equipment and environment. The operation and maintenance processing terminal is also used to adaptively generate corresponding operation and maintenance strategies based on the current operating status and the remaining lifespan, and send the operation and maintenance strategies to relevant personnel to prompt them to perform corresponding operation and maintenance work.

[0007] In one embodiment, before the operation and maintenance processing terminal simulates the current operating status of the mine equipment using a preset joint digital twin model of mine equipment and environment based on the component operating parameters, the operating status, and the environmental parameters: Obtain complete machine sample data and component sample data of the mining equipment, and obtain environmental sample data of the environment in which the mining equipment is located; Based on the sample data of the complete equipment, the sample data of the equipment components, and the environmental sample data, a digital twin model of the mine environment, a digital twin model of the mine equipment, and a digital twin model of the equipment components are constructed respectively. The digital twin model of the mine environment, the digital twin model of the mine equipment, and the digital twin model of the equipment components are integrated into a joint digital twin model of the mine equipment and environment.

[0008] In one embodiment, when the operation and maintenance processing terminal integrates the digital twin model of the mine environment, the digital twin model of the mine equipment, and the digital twin model of the equipment components into a joint digital twin model of the mine equipment and environment: Obtain the data association attribute label between any two of the three data sources: the complete equipment sample data, the equipment component sample data, and the environmental sample data; Based on the data association attribute tags, determine the dynamic change coefficient of any two of the three data points—the whole equipment sample data, the equipment component sample data, and the environmental sample data—under different value ranges; Based on the data association attribute tags and the data dynamic change coefficients, the digital twin model of the mine environment, the digital twin model of the mine equipment, and the digital twin model of the equipment components are merged into a joint digital twin model of the mine equipment and environment.

[0009] In one embodiment, when the operation and maintenance processing terminal determines the dynamic change coefficient of any two of the three data sets—the whole equipment sample data, the equipment component sample data, and the environmental sample data—under different value ranges based on the data association attribute tags: Based on the data association attribute tags, determine the influencing factors between the data, determine the changing trend of the influencing factors under different value ranges of the data, and divide the value ranges of multiple influencing factors according to the changing trend. Based on the median value of the value range, determine multiple sets of dynamic change coefficients for any two of the three data sets: the whole machine sample data, the equipment component sample data, and the environmental sample data.

[0010] In one embodiment, when the operation and maintenance processing terminal simulates the current operating status of the mine equipment and predicts the remaining lifespan of the mine equipment using a preset joint digital twin model of the mine equipment and environment based on the component operating parameters, the operating status, and the environmental parameters: Based on the component operating parameters, the operating status, and the environmental parameters, the required dynamic change coefficients of the data under the current conditions are determined through a preset joint digital twin model of mine equipment and environment. Based on the dynamic change coefficients of the data required under the current conditions, the current operating status of the mining equipment is simulated, and the remaining lifespan of the mining equipment is predicted.

[0011] In one embodiment, before the data acquisition terminal uploads the component operating parameters, the operating status, and the environmental parameters to the maintenance processing terminal: The operation and maintenance processing terminal is also used to obtain the first device code of the local operation and maintenance processing device, the second device code of the data acquisition terminal, and the management number between the local operation and maintenance processing device and the data acquisition terminal, wherein the management number is a connection relationship number that is customized when the local operation and maintenance processing device maintains communication connections with multiple data acquisition terminals at the same time. The operation and maintenance processing terminal is also used to generate a communication key based on the first device code, the second device code, and the management number, so that the data acquisition terminal can conduct encrypted communication with the operation and maintenance processing terminal based on the communication key.

[0012] In one embodiment, when the operation and maintenance processing terminal adaptively generates a corresponding operation and maintenance strategy based on the current operating status and the remaining lifespan: The operating protection threshold of the mining equipment is updated based on the remaining lifespan and the current operating status. Based on the updated operational protection threshold and the current operational status, an adaptive operation and maintenance strategy is generated.

[0013] In one embodiment, when the operation and maintenance processing terminal updates the operation protection threshold of the mining equipment based on the remaining lifespan and the current operating status: Obtain the historical operating status obtained from the previous simulation using the joint digital twin model of the mine equipment and environment, and compare and analyze the current operating status with the historical operating status; Based on the results of the comparative analysis and the remaining lifespan, the current maximum load capacity of the mining equipment is predicted; Update the operating protection threshold corresponding to the mining equipment based on the maximum load capacity.

[0014] In one embodiment, when the operation and maintenance processing terminal adaptively generates a corresponding operation and maintenance strategy based on the updated operation protection threshold and the current operation status: Based on the operational protection threshold and the current operational status, predict whether the mining equipment has operational safety risks and predict the corresponding operational safety level of the mining equipment; Based on the predicted security risk results and the stated operational security level, corresponding operation and maintenance strategies are adaptively generated.

[0015] In one embodiment, before the operation and maintenance processing terminal updates the operation protection threshold of the mine equipment based on the remaining lifespan and the current operating status, the method further includes: Determine whether the magnitude of the change in the operation protection threshold during this update exceeds the preset update magnitude; If the threshold is not exceeded, the operation protection threshold for this update will be optimized according to the preset update standard range value.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: A digital twin-based mine equipment lifespan prediction and maintenance system includes: a data acquisition terminal and an operation and maintenance processing terminal; the data acquisition terminal is used to acquire the operating data of the mine equipment and the environmental parameters of the environment in which the mine equipment is located, and upload the component operating parameters, the operating status, and the environmental parameters to the operation and maintenance processing terminal, wherein the operating data includes the component operating parameters of each component within the mine equipment and the operating status of the mine equipment; the operation and maintenance processing terminal is used to simulate the current operating status of the mine equipment based on the component operating parameters, the operating status, and the environmental parameters using a preset joint digital twin model of the mine equipment and environment, and predict the remaining lifespan of the mine equipment; the operation and maintenance processing terminal is also used to... The system describes the current operating status and remaining lifespan, adaptively generates corresponding operation and maintenance strategies, and sends these strategies to relevant personnel to prompt them to perform appropriate maintenance work. Specifically, it uses a pre-defined joint digital twin model of mine equipment and environment to comprehensively integrate the operating status of the mine equipment, the component operating parameters of each part within the equipment, and the environmental parameters of the environment in which the equipment is located. Through simulation and lifespan prediction using this joint digital twin model, multi-dimensional data can be integrated to achieve more accurate lifespan prediction. Based on this, corresponding operation and maintenance strategies are adaptively generated to address changes in the lifespan of the mine equipment under different data and parameter influences. This helps relevant personnel quickly and reasonably implement appropriate safety measures, thereby ensuring the effectiveness of mine equipment operation and maintenance. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the architecture of the mining equipment life prediction and maintenance system based on digital twins in this application; Figure 2 This is a schematic diagram illustrating the process of constructing a joint digital twin model of the mine equipment and environment for this application.

[0020] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0022] In one embodiment of this application, reference is made to Figure 1 The digital twin-based mine equipment life prediction and maintenance system includes: a data acquisition terminal and an operation and maintenance processing terminal; The data acquisition terminal is used to acquire the operating data of the mining equipment and the environmental parameters of the environment in which the mining equipment is located, and to upload the component operating parameters, the operating status and the environmental parameters to the operation and maintenance processing terminal. The operating data includes the component operating parameters of each component in the mining equipment and the operating status of the mining equipment. The operation and maintenance processing terminal is used to simulate the current operating status of the mine equipment and predict the remaining lifespan of the mine equipment based on the component operating parameters, the operating status and the environmental parameters through a preset joint digital twin model of mine equipment and environment. The operation and maintenance processing terminal is also used to adaptively generate corresponding operation and maintenance strategies based on the current operating status and the remaining lifespan, and send the operation and maintenance strategies to relevant personnel to prompt them to perform corresponding operation and maintenance work.

[0023] Understandably, the digital twin-based mine equipment life prediction and maintenance system consists of a closed-loop workflow comprised of a data acquisition terminal and an operation and maintenance processing terminal.

[0024] The data acquisition terminal is deployed at the mine site and collects two types of core data in real time through a high-precision sensor network: first, the operating data of the mine equipment (including parameters such as vibration frequency, temperature and current of key components such as bearings and hydraulic cylinders, as well as the operating status such as the number of start-ups and shutdowns and load rate of the whole machine, and the overall status of the mine equipment, the overall temperature of the equipment and the overall vibration signal of the equipment); second, the environmental parameters of the roadway where the equipment is located (such as temperature, humidity, gas concentration and dust density).

[0025] The collected data is uploaded to the operation and maintenance processing terminal in real time through the industrial IoT edge gateway using an encrypted transmission protocol (with a flexible communication key design).

[0026] Among them, the operation and maintenance processing terminal, as the intelligent hub of the system, has dual core functions: first, it dynamically simulates the operating status of the equipment under specific working conditions through a pre-trained digital twin model of the mine equipment and environment; second, it predicts the remaining lifespan of the equipment based on the simulation results and generates targeted maintenance strategies.

[0027] The maintenance instructions generated by the maintenance processing terminal are automatically pushed to maintenance personnel through multiple channels such as WeChat, SMS and maintenance dashboards, forming a closed-loop management of "monitoring, prediction, decision-making and execution".

[0028] It should be noted that the entire system operates on a dual-engine model of data-driven and physical mechanisms. The data acquisition end acts as the system's sensory nerves, deploying a multimodal sensor network in the mine roadways. For example, vibration sensors are embedded inside the bearings of equipment to directly capture stress waves generated by microscopic wear; thermal imagers scan the surface temperature field of equipment to map changes in heat dissipation efficiency; and environmental monitoring stations continuously collect temperature and humidity gradients, dust concentration distribution, and methane diffusion dynamics in the roadways.

[0029] The aforementioned heterogeneous data is spatiotemporally aligned through industrial IoT edge nodes to form a unified snapshot of device and environmental status, which is then transmitted to the operation and maintenance processing end in real time.

[0030] It should also be noted that the operation and maintenance processing terminal, as the system's brain, is based on a joint digital twin model of mine equipment and environment. This model is not simply an overlay of the environment model and the equipment model, but rather achieves bidirectional interaction through a dynamic coupling interface. For example, when the environment model detects that the dust concentration in the roadway exceeds 80mg / m³, it will trigger the real-time decay of the motor heat dissipation efficiency parameter in the equipment model; conversely, when the equipment model outputs an abnormal temperature rise in the hydraulic system, it will drive the environment model to correct the local heat conduction equation.

[0031] This two-way feedback mechanism enables the digital twin to have the ability to adapt and evolve under working conditions, providing a physically realistic simulation environment for life prediction.

[0032] In this embodiment, before the operation and maintenance processing terminal simulates the current operating status of the mine equipment using a preset joint digital twin model of mine equipment and environment based on the component operating parameters, the operating status, and the environmental parameters: Obtain complete machine sample data and component sample data of the mining equipment, and obtain environmental sample data of the environment in which the mining equipment is located; Based on the sample data of the complete equipment, the sample data of the equipment components, and the environmental sample data, a digital twin model of the mine environment, a digital twin model of the mine equipment, and a digital twin model of the equipment components are constructed respectively. The digital twin model of the mine environment, the digital twin model of the mine equipment, and the digital twin model of the equipment components are integrated into a joint digital twin model of the mine equipment and environment.

[0033] Understandably, the construction of the pre-designed joint digital twin model of mine equipment and environment begins with the collection of multi-dimensional sample data. The system first extracts sample data from three levels from the historical database: whole equipment sample data (such as load records and fault logs over the years), equipment component sample data (such as bearing vibration waveforms and motor temperature rise curves), and environmental sample data (such as the spatiotemporal distribution of temperature and humidity in the roadway and dust concentration variation maps).

[0034] Furthermore, based on this data, the system constructs a three-layer twin model in stages: One is the digital twin model of the mine environment: it can use spatiotemporal convolutional neural networks to process environmental parameters and capture environmental evolution patterns such as temperature gradients and dust diffusion. For example, for high-temperature roadway areas, the model can establish thermodynamic transfer equations to predict the surface temperature distribution of equipment.

[0035] The digital twin model of the mine environment is essentially a digital mirror of the roadway. It uses a spatiotemporal graph convolution algorithm to reconstruct a three-dimensional environmental field from discrete temperature, humidity, and gas sensor data. For example, in a coal mining face, the model can simulate the diffusion path of dust airflow under ventilation disturbances and accurately calculate the dust deposition rate adhering to the heat sink of equipment, thereby quantifying the local impact of the environment on the equipment.

[0036] Secondly, digital twin models of equipment components are constructed based on physical degradation mechanisms. Taking the cutting bearing of a coal mining machine as an example, a remaining life prediction model based on the Wiener process is established by integrating vibration spectrum characteristics and material fatigue theory.

[0037] Among them, the digital twin model of equipment components focuses on the physical mechanisms of microscopic degradation. The key point of this model is to introduce environmentally sensitive parameters. For example, the humidity of the tunnel is used as a control variable for the thickness of the lubricating film, and the calculation of the corrosion acceleration factor is automatically triggered when the humidity is greater than 90%.

[0038] Thirdly, the digital twin model of mining equipment integrates the correlation of components through a system reliability block diagram. For example, it constructs a three-level fault propagation tree corresponding to the motor, gearbox and drum, and quantifies the impact weight of local failures on the overall function of the machine.

[0039] Among them, the digital twin model of mining equipment integrates component relationships from a systems engineering perspective. It establishes a digital mapping of fault propagation. For example, when bearing vibration exceeds the limit, the model automatically calculates its impact load transmission coefficient to the gearbox and assesses the degree of overall machine function degradation. This reveals how local failures are amplified into overall machine failures through the power chain via system-level modeling.

[0040] It should be noted that model fusion is a key breakthrough for achieving accurate predictions. The system achieves deep coupling of the environment, equipment, and components through the following steps: First, custom data association attribute labels: identify the inherent correlation between cross-domain data, such as labeling tunnel temperature and bearing wear as a non-linear positive correlation, and dust concentration and motor heat dissipation efficiency as a negative exponential relationship.

[0041] Secondly, the dynamic variation coefficient is calculated: for each associated label, the coupling strength changes in different parameter ranges are analyzed. Taking the effect of temperature on bearing wear as an example: when the temperature is in the baseline range of 20-30 degrees Celsius, the wear coefficient is set to 1.0; when the temperature rises to the danger range of 30-50 degrees Celsius, the coefficient increases exponentially by the function k=1.2×e^(0.05ΔT); when it exceeds 50 degrees Celsius, the coefficient is fixed at the limit value of 2.0.

[0042] In this embodiment, when the operation and maintenance processing terminal integrates the digital twin model of the mine environment, the digital twin model of the mine equipment, and the digital twin model of the equipment components into a joint digital twin model of the mine equipment and environment: Obtain the data association attribute label between any two of the three data sources: the complete equipment sample data, the equipment component sample data, and the environmental sample data; Based on the data association attribute tags, determine the dynamic change coefficient of any two of the three data points—the whole equipment sample data, the equipment component sample data, and the environmental sample data—under different value ranges; Based on the data association attribute tags and the data dynamic change coefficients, the digital twin model of the mine environment, the digital twin model of the mine equipment, and the digital twin model of the equipment components are merged into a joint digital twin model of the mine equipment and environment.

[0043] Understandably, in the process of constructing a joint digital twin model of mine equipment and environment, the system breaks through the limitations of traditional modeling by coupling the environment, equipment and components in multiple dimensions. Its core lies in establishing a two-layer driving architecture of data association attribute labels and data dynamic change coefficients, so that the originally independent mine environment model, equipment whole machine model and equipment component model can form an organic whole.

[0044] It should be noted that the essence of data association attribute tags is the digital expression of cross-domain data causal relationships. Through historical data mining and physical mechanism analysis, semantic association rules are assigned to any combination of equipment whole-machine data, component data, and environmental data. This allows the digital twin model of the mine environment, the digital twin model of the mine equipment, and the digital twin model of the equipment components to be merged into a joint digital twin model of the mine equipment and environment. For details, please refer to... Figure 2 .

[0045] Taking the hydraulic system of a coal mining machine as an example: The environmental parameter "tunnel dust concentration" and the component parameter "hydraulic valve core wear" are labeled as "positively correlated with abrasive erosion". This label is mainly based on the statistics of fault disassembly data. Specifically, when the dust concentration is greater than 80mg / m³, the valve core wear rate increases to 3.2 times the baseline value.

[0046] Among them, the "conveyor belt load rate" in the overall machine parameters and the "tunnel humidity" in the environmental parameters are marked with the "dynamic negative correlation" label. When the mine equipment is running under high load, the heat generated by the mine equipment can reduce the local humidity, but when the humidity is greater than 90%, it will cause the belt to slip and cause negative feedback.

[0047] It should be noted that these labels are not statically defined, but dynamically optimized through cross-domain correlation analysis algorithms: the system continuously compares the temporal correlation between environmental parameter fluctuations and equipment status changes. When a new correlation pattern is detected between sulfide concentration and motor corrosion rate in a copper mine roadway, a new label is automatically generated that sulfide concentration affects the motor winding corrosion rate, and it is assigned a corresponding initial weight.

[0048] In this embodiment, when the operation and maintenance processing terminal determines the dynamic change coefficient of any two of the three data sets—the whole equipment sample data, the equipment component sample data, and the environmental sample data—under different value ranges based on the data association attribute tags: Based on the data association attribute tags, determine the influencing factors between the data, determine the changing trend of the influencing factors under different value ranges of the data, and divide the value ranges of multiple influencing factors according to the changing trend. Based on the median value of the value range, determine multiple sets of dynamic change coefficients for any two of the three data sets: the whole machine sample data, the equipment component sample data, and the environmental sample data.

[0049] Understandably, the dynamic change coefficient is the core variable for quantifying the intensity of environmental disturbances, and its calculation follows a process of segmenting operating conditions, fitting trends, and fixing coefficients.

[0050] Among them, the extraction of influencing factors mainly analyzes the dominant factors for each associated label. For example, in the label of "temperature and bearing wear", it is determined that temperature is the core factor affecting the viscosity of lubricating oil and the thermal expansion of materials. Therefore, based on physical experiments and operational data, nonlinear response curves of influencing factors can be plotted. Specifically, in the bearing case, the effect of temperature on wear exhibits a three-stage characteristic: The safe range is 20 to 30 degrees Celsius, with the wear coefficient remaining stable at 1.0. Among them, 30 to 50 degrees Celsius is the accelerated degradation zone, and the coefficient increases exponentially with temperature. The specific growth function is k = 1.2 × e^(0.05ΔT). Among them, temperatures above 50 degrees Celsius are considered high-risk, with the coefficient jumping to 2.5 (due to lubrication failure caused by lubricant carbonization). Furthermore, based on this, the value range can be divided: the applicable range for the project is divided according to the trend inflection point, the boundaries of the above temperature range (30 degrees Celsius, 50 degrees Celsius) are set as the segment threshold, and the characteristic value (such as the coefficient 1.8 corresponding to 40 degrees Celsius) is taken in each range as the calibration benchmark for typical working conditions.

[0051] It should be noted that the design of piecewise functions is mainly a digital mapping of physical failure mechanisms. In the actual working conditions of mining equipment, the influence of environmental parameters on the equipment often has a critical threshold. For example, the 30-degree Celsius temperature threshold corresponds to the pour point of mineral lubricating oil, and the oil film remains stable below this temperature. Another example is that the 50-degree Celsius temperature threshold is close to the glass transition point of sealing rubber materials, which leads to accelerated seal failure.

[0052] Therefore, three levels of zones can be defined: a safe zone (green), a warning zone (yellow), and a danger zone (red), so that the dynamic coefficients can accurately match the actual physical process.

[0053] In this embodiment, when the operation and maintenance processing terminal simulates the current operating status of the mine equipment and predicts the remaining lifespan of the mine equipment based on the component operating parameters, the operating status, and the environmental parameters using a preset joint digital twin model of the mine equipment and environment: Based on the component operating parameters, operating status, and environmental parameters, the required dynamic data change coefficients under the current conditions are determined through a preset digital twin model of the mine equipment and environment; based on the required dynamic data change coefficients under the current conditions, the current operating status of the mine equipment is simulated, and the remaining lifespan of the mine equipment is predicted.

[0054] Understandably, during the equipment condition simulation phase, the operation and maintenance processing terminal inputs real-time collected component parameters (such as vibration velocity of 5.2 mm / s) and environmental parameters (such as temperature of 45 degrees Celsius and dust concentration of 80 mg / m³) into the joint digital twin model of the mine equipment and environment. This joint digital twin model first calls a pre-set association rule library, for example, retrieving the dynamic coefficient corresponding to the "temperature and bearing wear" label as 1.8, and based on this, performs working condition correction on the original vibration data: effective vibration value = 5.2 × 1.8 = 9.36 mm / s. The corrected parameters drive the operation of the equipment digital twin, outputting key status indicators such as the current health index, thereby further calculating the remaining life of the mine equipment.

[0055] Specifically, for example, the daily decline rate of the health index under high temperature conditions can increase from a baseline of 0.008 per day to 0.015 per day. When the model calculates that the current health index is 62 (threshold = 40) and the degradation rate is 0.012 per day, the predicted remaining lifespan is approximately (62-40) / (0.012×100)≈18.3 days.

[0056] Furthermore, the adaptive generation of operation and maintenance strategies reflects the system's intelligent decision-making capabilities. The strategy engine can be configured with a built-in fault evolution knowledge base. When it predicts that the remaining lifespan of the coal mining machine's cutting bearing is less than 7 days, the system not only generates an "immediately replace bearing" instruction but also analyzes the wear causes using an environmental model. Specifically, if it identifies that the local temperature consistently exceeds 50 degrees Celsius, it will simultaneously trigger an environmental management work order to "enhance roadway ventilation." A more advanced decision-making logic is reflected in risk prevention and control: when the gas concentration monitoring value exceeds the corresponding threshold and the motor temperature rises abnormally, the system automatically executes a safety interlock protocol, first reducing the equipment load and initiating emergency ventilation before dispatching maintenance tasks.

[0057] In addition, the operation and maintenance strategy generation module implements tiered responses based on the prediction results: When the remaining lifespan is less than 7 days and the health index is less than 50, an emergency maintenance protocol is triggered: an instruction to "stop the machine immediately, replace the bearings and clean the environment" is automatically generated, and a red alert is simultaneously pushed to the mobile phones of relevant personnel and / or the central control screen.

[0058] When the remaining life expectancy is greater than 30 days but the daily decline in the health index is greater than 5%, a preventive maintenance procedure is initiated: the maintenance window for the following week is planned, and a work order for "strengthening temperature monitoring and special testing of the hydraulic system" is generated and dispatched to the regional maintenance team.

[0059] In special environmental scenarios (such as when gas concentration exceeds the limit), the system will forcibly intervene in equipment control and implement proactive protection strategies such as load reduction operation or safe shutdown.

[0060] In this embodiment, before the data acquisition terminal uploads the component operating parameters, operating status, and environmental parameters to the maintenance processing terminal: The operation and maintenance processing terminal is also used to obtain the first device code of the local operation and maintenance processing device, the second device code of the data acquisition terminal, and the management number between the local operation and maintenance processing device and the data acquisition terminal, wherein the management number is a connection relationship number that is customized when the local operation and maintenance processing device maintains communication connections with multiple data acquisition terminals at the same time. The operation and maintenance processing terminal is also used to generate a communication key based on the first device code, the second device code, and the management number, so that the data acquisition terminal can conduct encrypted communication with the operation and maintenance processing terminal based on the communication key.

[0061] In this embodiment, before encrypted communication is implemented between the local processing devices corresponding to the data acquisition end and the operation and maintenance processing end, it is necessary to determine the communication key used for interaction between the two. This communication key can be randomly generated or formulated by staff. In order to ensure the uniqueness of this key, in this embodiment, the actual required communication key is generated based on the relevant information corresponding to the local processing devices of the data acquisition end and the operation and maintenance processing end.

[0062] Specifically, the first device code of the local processing device and the second device code of the data acquisition terminal can be obtained first. The communication key can be generated from the first device code and the second device code. For example, if the first device code is 202106193489 and the second device code is 202412309857, the communication key can be generated by arranging or combining the first device code and the second device code, or by adding or multiplying the two device codes.

[0063] Furthermore, in this embodiment, the local processing device may manage multiple data acquisition terminals simultaneously. Therefore, in order to avoid multiple data acquisition terminals using the same communication key at the same time, and to prevent security issues from arising for all data acquisition terminals if the communication key at any one data acquisition terminal is lost, in this embodiment, in addition to obtaining the corresponding first device code and second device code, a unique communication key for each data acquisition terminal is generated based on the location information of each data acquisition terminal.

[0064] Specifically, after obtaining the first device code and the second device code, the management code corresponding to the local processing device and the data acquisition terminal is obtained, and the corresponding communication key is generated by combining the above three codes. The process of generating the communication key is the same as the process of generating the first device code and the second device code alone, and will not be described again here.

[0065] The management code is a custom connection number set when the local operation and maintenance process maintains communication connections with multiple data acquisition terminals. For example, if the local operation and maintenance device has communication connections with data acquisition terminals A, B, and C, the local operation and maintenance device can assign A, B, and C the corresponding numbers 1, 2, and 3, respectively.

[0066] In this embodiment, the encrypted data sent from the data acquisition terminal to the operation and maintenance processing terminal is encrypted using a communication key between the data acquisition terminal and the operation and maintenance processing terminal. This communication key is used for encryption every time the two parties exchange information. This communication key is stored as pre-existing information in the data acquisition terminal and the operation and maintenance processing terminal and can be edited and saved by the corresponding administrators, thus ensuring the security of communication between the local processing devices corresponding to the data acquisition terminal and the operation and maintenance processing terminal.

[0067] In this embodiment, when the operation and maintenance processing terminal adaptively generates the corresponding operation and maintenance strategy based on the current operating status and the remaining lifespan: Based on the remaining lifespan and the current operating status, update the operating protection threshold of the mining equipment; based on the updated operating protection threshold and the current operating status, adaptively generate corresponding operation and maintenance strategies.

[0068] Understandably, in this embodiment, a preset digital twin model of mine equipment and environment will be used to simulate the actual current operating status of the mine equipment under the current operating data state. However, it is necessary to analyze different current operating conditions. For example, the remaining lifespan of the mine equipment can be estimated from the maximum load capacity of each mine equipment under the current operating conditions and the duration of the maximum load process, so as to facilitate better operation and maintenance of the mine equipment.

[0069] It should be noted that, in order to ensure better operation and maintenance of mining equipment, a dynamically updated operation protection threshold parameter is introduced in this embodiment. By comparing the operation protection threshold with the current operation status in the same dimension, the actual usage status of the mining equipment can be monitored as the remaining lifespan of the mining equipment continues to decrease. If the current operation status does not meet the corresponding requirements of the operation protection threshold, the corresponding operation and maintenance strategy can be adaptively generated.

[0070] It is important to understand that the remaining lifespan of mining equipment is reduced due to continuous operation and component wear. For example, brand-new mining equipment can operate at maximum load for extended periods, but equipment used for two years may not meet this requirement (e.g., unable to operate at maximum load, or unable to operate continuously for extended periods due to component aging or damage to major parts). Therefore, it is necessary to develop corresponding implementation standards for the remaining lifespan of different mining equipment. For instance, for older equipment, its operating power and duration should be reduced, and the stable operating parameters of aging equipment should be monitored more closely (specifically, highly sensitive monitoring indicators can be set) to ensure the proper use of aging equipment. Thus, corresponding operating protection thresholds can be established based on the remaining lifespan of mining equipment. By simulating the changes in the remaining lifespan of mining equipment under current operating conditions, these operating protection thresholds can be further updated, thereby achieving precise operation and maintenance for mining equipment with different lifespans.

[0071] Specifically, the operation protection thresholds may include thresholds for multiple dimensions such as maximum operating time, component vibration signal threshold, maximum power load, equipment operating temperature, and ambient humidity. For the above operation protection thresholds, the current operating conditions can be compared with the simulated conditions to determine whether the operation requirements are met. Based on this judgment, corresponding operation and maintenance strategies can be generated adaptively. For example, if any of the following does not meet the corresponding operation protection threshold: long continuous operating time of the equipment, excessively high temperature of important parts of the equipment, or ambient humidity of the equipment (operation protection thresholds are set in three dimensions: the whole machine of the mining equipment, the equipment components, and the environment in which the equipment is located, and corresponding operation and maintenance strategies are generated based on these three aspects), an operation and maintenance strategy can be generated based on the non-compliance.

[0072] In this embodiment, when the operation and maintenance processing terminal updates the operation protection threshold of the mine equipment based on the remaining lifespan and the current operating status: The system obtains the historical operating status obtained from the previous simulation using the joint digital twin model of the mine equipment and environment, and compares and analyzes the current operating status with the historical operating status; based on the results of the comparison and analysis and the remaining lifespan, it predicts the current maximum load capacity of the mine equipment; and updates the corresponding operating protection threshold of the mine equipment based on the maximum load capacity.

[0073] Understandably, after the local processing equipment at the operation and maintenance end obtains the historical operating status simulated by the joint digital twin model of the mine equipment and environment, it needs to conduct an actual analysis of the operating status of the mine equipment based on the historical operating status, and infer the actual operating losses and lifespan losses of the mine equipment. On this basis, it determines the actual operating status of the mine equipment in the past operating cycle, and predicts the maximum load capacity of the mine equipment based on the future operating trend and the current remaining lifespan of the mine equipment, thereby updating the corresponding operation protection threshold based on this data.

[0074] The process of simulating the current operating status mainly includes constructing a digital model based on digital twin technology to simulate the operating status of mining equipment in a specific environment, and importing various historical operating conditions into the model to simulate the current operating status of the mining equipment within the operating cycle corresponding to the historical operating conditions. The current operating status is used to represent the state parameters characterized by the continuously operating mining equipment within the current operating cycle, such as output voltage rating, output fluctuation amplitude, and changes in temperature and humidity of the operating environment.

[0075] Understandably, the local processing device in this embodiment will periodically simulate historical operating conditions, and the local processing device at the operation and maintenance end will optimize and adjust the operating protection threshold of the mine equipment in each cycle. Thus, an operating condition simulated based on historical operating conditions can be generated in each cycle. Therefore, in this embodiment, the current operating condition and the operating condition obtained from the previous simulation can be compared and analyzed horizontally, thereby analyzing the overall deviation of the operating condition of the mine equipment in the two cycles.

[0076] It should be noted that when comparing the operating conditions obtained from the previous simulation with the current operating conditions, the main comparison is made of the overall operating conditions of the simulated mine equipment, such as the overall difference in the operating environment (environmental data such as temperature, humidity, and operating time) and the overall amplitude curve of the output voltage during operation, rather than using the peak and valley values ​​in the operating cycle as the comparison target.

[0077] Furthermore, it is also necessary to conduct corresponding comparative analyses on key monitoring components in the environment and mining equipment.

[0078] In this embodiment, after obtaining the results of the comparative analysis, the results can be used as a basis to analyze the operational differences in different cycles and determine the operational trends of the mine equipment in the two operating cycles, including increased overload operating time and increased additional power consumption due to continuously increasing ambient temperature. Based on the results of the comparative analysis, the risks that the mine equipment may face in future operation can be predicted, and these risks can be used as a reference to update the corresponding operating protection thresholds of the mine equipment. For example, if the ambient temperature is continuously too high and the line loss is severe during the operation of the mine equipment, the threshold for the power output of the mine equipment needs to be lowered accordingly to ensure that the operational risks of the mine equipment can be detected in advance.

[0079] Understandably, when predicting risks arising during the operation of mining equipment, the main approach involves analyzing the overall operating status of the equipment, the operating status of key monitored components, and the actual environment in which the equipment is located. This analysis examines the overall operating trend of the equipment over two consecutive operating cycles. For example, rising temperatures necessitate increased rated power output, or increased electricity demand leads to overload operation. Based on this data, the lifespan of each component is predicted during these two operating cycles. For instance, under sustained high temperatures and high power output, the internal wiring of the equipment may overheat and become damaged. Therefore, the lifespan loss during operation can be predicted based on comparative analysis. This prediction process can be quantified by relevant personnel; for example, if a circuit is exposed to high temperatures for 24 hours, its lifespan loss increases by 0.01%.

[0080] In this embodiment, when the operation and maintenance processing terminal adaptively generates the corresponding operation and maintenance strategy based on the updated operation protection threshold and the current operation status: Based on the operational protection threshold and the current operational status, predict whether there are operational safety risks to the mining equipment and predict the corresponding operational safety level of the mining equipment; based on the predicted safety risk results and the operational safety level, adaptively generate the corresponding operation and maintenance strategy.

[0081] Understandably, in this embodiment, by setting an operational protection threshold, different operation and maintenance strategies are formulated for mine equipment under different lifespan states and different operating conditions. The operational protection threshold is used as an evaluation standard, and different threshold standards can be set for different operating parameters. The current operating condition is evaluated and predicted using the threshold standard to determine whether there is an operational safety risk in the current mine, predict the corresponding operational safety level of the mine equipment, and adaptively generate the corresponding operation and maintenance strategy based on the two predicted results.

[0082] Specifically, based on the actual size of the operational protection threshold, corresponding risk levels and safety risk assessment ranges can be set. For example, taking temperature as an example, when the ambient temperature of the mining equipment is too high, the equipment faces certain operational risks. That is, when the temperature exceeds a certain threshold, there is a safety risk. This temperature can be used as an intermediate value; the portion above this temperature is considered to have a safety risk, while the portion below it is considered to have no safety risk. The temperature range above this threshold can be further divided: the temperature range 1.2 times higher than the threshold is considered high risk; the temperature range between the threshold and 1.2 times the threshold is considered medium risk; the temperature range 0.8 times lower than the threshold is considered risk-free; and the temperature range between 0.8 times and the threshold is considered low risk. Therefore, different operation and maintenance strategies can be generated for different risk levels.

[0083] Specifically, the low-risk zone prompts relevant personnel to perform timely maintenance, the medium-risk zone prompts relevant personnel to immediately go to the site for inspection, and the high-risk zone prompts relevant personnel to immediately shut down the equipment for repair.

[0084] In this embodiment, before the operation and maintenance processing terminal updates the operation protection threshold of the mine equipment based on the remaining lifespan and the current operating status, the method further includes: Determine whether the change in the operation protection threshold during this update exceeds a preset update value; if it does not exceed the preset update standard range, optimize the operation protection threshold during this update.

[0085] It should be noted that when updating the operation protection threshold, it is necessary to consider whether the adjustment range of the operation protection threshold after each update is too large. If the adjustment range is too large, it may affect the abnormality of the life monitoring operation and maintenance scheme of the operation and maintenance processing terminal, thereby affecting the daily work of the corresponding operation and maintenance personnel. Therefore, in this embodiment, it is also necessary to set the corresponding update limit logic of the operation protection threshold, specifically controlling the actual update range of the operation protection threshold for each update.

[0086] In this embodiment, after each update of the operation protection threshold, it is determined whether the value corresponding to the updated operation protection threshold is within the preset update standard range. If it is within the range, the updated operation protection threshold needs to be updated and optimized so that its optimized value falls within the preset update standard range.

[0087] The optimization process mainly involves determining which value among the maximum and minimum values ​​in the preset update standard range is closest to the updated operation protection threshold, and then optimizing the updated operation protection threshold to one of the closest extreme values ​​in the range.

[0088] This embodiment of a digital twin-based mine equipment life prediction and maintenance system includes: a data acquisition terminal and an operation and maintenance processing terminal; the data acquisition terminal is used to acquire the operating data of the mine equipment and the environmental parameters of the environment in which the mine equipment is located, and upload the component operating parameters, the operating status, and the environmental parameters to the operation and maintenance processing terminal, wherein the operating data includes the component operating parameters of each component within the mine equipment and the operating status of the mine equipment; the operation and maintenance processing terminal is used to simulate the current operating status of the mine equipment based on the component operating parameters, the operating status, and the environmental parameters using a preset joint digital twin model of the mine equipment and environment, and predict the remaining life of the mine equipment; the operation and maintenance processing terminal is also used to predict the remaining life of the mine equipment based on the current operating status and the remaining... The system adaptively generates corresponding operation and maintenance strategies based on the lifespan of the mine equipment and sends these strategies to relevant personnel to prompt them to perform corresponding maintenance work. Specifically, it integrates the operating status of the mine equipment, the component operating parameters of each part within the equipment, and the environmental parameters of the environment in which the equipment is located, using a pre-defined joint digital twin model of the mine equipment and its environment. Through simulation and lifespan prediction using this joint digital twin model, multi-dimensional data can be integrated to achieve more accurate lifespan prediction. Based on this, the system adaptively generates corresponding operation and maintenance strategies for changes in the lifespan of the mine equipment under different data and parameter influences. This helps relevant personnel quickly and reasonably implement appropriate safety measures, thereby ensuring the effectiveness of mine equipment operation and maintenance.

[0089] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

[0090] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0091] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0092] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A mining equipment life prediction and maintenance system based on digital twins, characterized in that, The digital twin-based mine equipment life prediction and maintenance system includes a data acquisition terminal and an operation and maintenance processing terminal. The data acquisition terminal acquires the operating data of the mine equipment and the environmental parameters of the environment in which the mine equipment is located. It then uploads the component operating parameters, the operating status, and the environmental parameters to the operation and maintenance processing terminal. The operating data includes the component operating parameters of each component within the mine equipment and the operating status of the mine equipment. The operation and maintenance processing terminal simulates the current operating status of the mine equipment based on the component operating parameters, the operating status, and the environmental parameters using a preset joint digital twin model of the mine equipment and environment, and predicts the remaining lifespan of the mine equipment. The operation and maintenance processing terminal also adaptively generates corresponding operation and maintenance strategies based on the current operating status and the remaining lifespan, and sends the operation and maintenance strategies to relevant personnel to prompt them to perform corresponding operation and maintenance work.

2. The system as described in claim 1, characterized in that, Before the operation and maintenance processing terminal simulates the current operating status of the mine equipment using a preset joint digital twin model of mine equipment and environment based on the component operating parameters, operating status, and environmental parameters: It acquires sample data of the entire mine equipment and sample data of its components, and environmental sample data of the environment in which the mine equipment is located; based on the sample data of the entire equipment, the sample data of its components, and the environmental sample data, it constructs a digital twin model of the mine environment, a digital twin model of the mine equipment, and a digital twin model of its components, respectively; and it integrates the digital twin model of the mine environment, the digital twin model of the mine equipment, and the digital twin model of its components into a joint digital twin model of mine equipment and environment.

3. The system as described in claim 2, characterized in that, When integrating the mine environment digital twin model, the mine equipment digital twin model, and the equipment component digital twin model into a joint digital twin model of mine equipment and environment at the operation and maintenance processing end: Data association attribute tags are obtained between any two of the equipment whole machine sample data, the equipment component sample data, and the environmental sample data; based on the data association attribute tags, the dynamic change coefficient of data between any two of the equipment whole machine sample data, the equipment component sample data, and the environmental sample data under different value ranges is determined; based on the data association attribute tags and the dynamic change coefficient, the mine environment digital twin model, the mine equipment digital twin model, and the equipment component digital twin model are merged into a joint digital twin model of mine equipment and environment.

4. The system as described in claim 3, characterized in that, When determining the dynamic change coefficient of any two of the equipment whole sample data, equipment component sample data, and environmental sample data under different value ranges based on the data association attribute tags at the operation and maintenance processing end: the influencing factors between each data are determined based on the data association attribute tags, and the changing trend of the influencing factors under different value ranges of the data is determined, and multiple value intervals of the influencing factors are divided based on the changing trend; multiple sets of dynamic change coefficients of any two of the equipment whole sample data, equipment component sample data, and environmental sample data are determined based on the median value of the value intervals.

5. The system as described in claim 4, characterized in that, When the operation and maintenance processing terminal simulates the current operating status of the mine equipment and predicts the remaining lifespan of the mine equipment based on the component operating parameters, the operating status, and the environmental parameters using a preset joint digital twin model of mine equipment and environment: Based on the component operating parameters, the operating status, and the environmental parameters, the preset joint digital twin model of mine equipment and environment determines the required dynamic data change coefficient under the current conditions; based on the required dynamic data change coefficient under the current conditions, the current operating status of the mine equipment is simulated, and the remaining lifespan of the mine equipment is predicted.

6. The system as described in claim 1, characterized in that, Before the data acquisition terminal uploads the component operating parameters, operating status, and environmental parameters to the maintenance processing terminal: the maintenance processing terminal is further configured to obtain a first device code of the local maintenance processing device, a second device code of the data acquisition terminal, and a management number between the local maintenance processing device and the data acquisition terminal, wherein the management number is a custom-defined connection relationship number when the local maintenance processing device maintains communication connections with multiple data acquisition terminals simultaneously; the maintenance processing terminal is further configured to generate a communication key based on the first device code, the second device code, and the management number, so that the data acquisition terminal can perform encrypted communication with the maintenance processing terminal based on the communication key.

7. The system as described in claim 1, characterized in that, When the operation and maintenance processing terminal adaptively generates the corresponding operation and maintenance strategy based on the current operating status and the remaining lifespan: the operation protection threshold of the mining equipment is updated based on the remaining lifespan and the current operating status; Based on the updated operational protection threshold and the current operational status, an adaptive operation and maintenance strategy is generated.

8. The system as described in claim 7, characterized in that, When the operation and maintenance processing terminal updates the operation protection threshold of the mine equipment based on the remaining lifespan and the current operating status: it obtains the historical operating status obtained by the previous simulation through the joint digital twin model of the mine equipment and environment, and compares and analyzes the current operating status with the historical operating status; based on the comparison and analysis results and the remaining lifespan, it predicts the current maximum load capacity of the mine equipment; and updates the corresponding operation protection threshold of the mine equipment based on the maximum load capacity.

9. The system as described in claim 7, characterized in that, When the operation and maintenance processing terminal adaptively generates corresponding operation and maintenance strategies based on the updated operation protection threshold and the current operation status: based on the operation protection threshold and the current operation status, it predicts whether there is an operation safety risk of the mine equipment and predicts the corresponding operation safety level of the mine equipment; Based on the predicted security risk results and the stated operational security level, corresponding operation and maintenance strategies are adaptively generated.

10. The system as described in claim 7, characterized in that, Before the operation and maintenance processing terminal updates the operation protection threshold of the mine equipment based on the remaining lifespan and the current operating status, the process further includes: determining whether the update change amplitude of the operation protection threshold exceeds a preset update amplitude; if it does not exceed the preset update standard range value, optimizing the updated operation protection threshold.

Images