Hard rock mining equipment health diagnosis system and method based on digital twinning

Abstract

The application discloses a hard rock mining equipment health diagnosis system and method based on digital twinning, and belongs to the technical field of quality detection. In the application, corresponding equipment mining condition data and hard rock condition data are acquired based on a data acquisition terminal, a data twinning software is used to generate a data twinning model of a mining process, abnormal analysis of a drill bit state is performed based on the equipment mining condition and damage condition of the equipment mining drill bit, hard rock mining risk analysis of a future position is performed based on the hardness condition and shape condition of the hard rock of the future position, future mining health analysis is performed based on the abnormal analysis result of the drill bit state and the future position hard rock mining risk analysis result, and mining health early warning is performed based on the future mining health analysis result. Real-time health evaluation and grading early warning of the hard rock mining process are realized, and the mining safety and the intelligent level are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of quality detection, and in particular to a hard rock mining equipment health diagnosis system and method based on digital twinning. BACKGROUND

[0002] In the process of hard rock mining, key components such as drill bits and cutters work continuously under high pressure, high impact and complex geological conditions. Their wear, damage and failure are important factors affecting mining efficiency and safety. Existing mining equipment monitoring mainly relies on single sensor data (such as torque, current, vibration, etc.), which is difficult to fully reflect the complex coupling relationship between equipment operating state and geological conditions. On the other hand, there is uneven strength distribution, joint fissure and complex morphology in hard rock strata. When the drill bit enters the rock stratum at the future location, sudden situations such as drill sticking, cutter breaking and drilling efficiency dropping may occur. The existing method lacks the ability to jointly predict the future drilling risk and equipment state.

[0003] The application not only considers the current equipment state, but also combines the geological data of the future drilling direction to establish a hard rock morphology index, a rock surface protrusion risk index and a comprehensive risk index, define a future mining health index, and through the construction of a digital twinning integrated model of equipment state and future rock mass risk, real-time health assessment and graded early warning of the hard rock mining process are realized, and the mining safety, intelligent level and economic benefit are improved. SUMMARY

[0004] In view of the deficiencies of the prior art, the application provides a hard rock mining equipment health diagnosis system and method based on digital twinning

[0005] To achieve the above purpose, the application provides the following technical solutions:

[0006] The hard rock mining equipment health diagnosis method based on digital twinning includes the following specific steps:

[0007] Step S1, obtaining corresponding equipment mining situation data and hard rock situation data based on a data acquisition terminal, and generating a mining process data twinning model through data twinning software;

[0008] Step S2, performing drill bit state anomaly analysis based on equipment mining situation and drill bit damage situation of equipment mining;

[0009] Step S3, performing future location hard rock mining risk analysis based on the hardness and shape of the future location hard rock;

[0010] Step S4, performing future mining health analysis based on the drill bit state anomaly analysis result and the future location hard rock mining risk analysis result;

[0011] Step S5, based on the future mining health analysis result, mining health early warning is carried out.

[0012] Preferably, the data acquisition terminal acquires corresponding equipment mining situation data and hard rock situation data, and generates a mining process data twin model through data twin software, which includes the following specific steps:

[0013] S11, the hard rock mining equipment mining situation and equipment data are acquired through the data acquisition terminal, including the wear depth, wear area, drill bit speed, cutting force and thrust force of the drill bit, and the hard rock data are acquired through geological exploration means, and the uniaxial compressive strength, rock hardness, fracture distribution and spatial structure of the front are acquired through methods such as seismic wave reflection, acoustic wave transmission and geological radar;

[0014] S12, the mining situation, equipment data and hard rock data are input into the digital twin software, combined with the equipment physical model and the mining process simulation, to generate a real-time updated mining process digital twin model, which is used to reflect the interactive state of the equipment and the rock mass, the equipment sub-model is used to describe the mechanical and energy transmission process of each component of the mining equipment, and the rock mass sub-model is used to describe the stress, fracture propagation and crushing law of the rock mass in the drilling process.

[0015] Preferably, the drill bit state abnormality analysis based on the equipment mining situation and the damage situation of the equipment mining drill bit includes the following specific steps:

[0016] S21, the wear depth and wear area of the drill bit blade of the mining equipment are substituted into the drill bit damage degree evaluation formula to evaluate the drill bit health condition, wherein the drill bit damage degree evaluation formula is: wherein D is the initial thickness of the blade, is the current blade thickness, A is the surface area of the blade tip, is the blade tip wear area, and are the depth weight coefficient and the area weight coefficient respectively, and the greater the drill bit damage degree, the more serious the drill bit wear;

[0017] S22, the property parameters and operating parameters of the drill bit blade of the mining equipment are substituted into the blade sharpness evaluation formula to evaluate the cutting ability of the blade, wherein the blade sharpness evaluation formula is: wherein, is the cutting force, is the thrust force, n is the drill bit speed, is the blade geometric sharpness coefficient, which is obtained through the blade tip factory parameter, Y is the blade material hardness, which is obtained from the blade specification, wherein the property parameters of the drill bit blade of the mining equipment include the blade geometric sharpness coefficient and the blade material hardness, and the operating parameters include the cutting force, the thrust force and the drill bit speed;

[0018] S23, statistics of the particle size distribution of the rock crushed by the mining equipment, the standard deviation of the particle size of the crushed rock is calculated, and the standard deviation of the particle size distribution of the rock is used to measure the crushing uniformity, the larger the standard deviation, the worse the mining uniformity;

[0019] S24, the drill bit torque and angular velocity of the mining equipment are substituted into the rock breaking power evaluation formula to evaluate the unit rock volume breaking power, wherein the rock breaking power evaluation formula is: , wherein T is the drill bit torque, v is the angular velocity, is the volume of the rock broken per unit time, and the larger the power, the higher the equipment load, the lower the tool efficiency or the more serious drill bit wear;

[0020] S25, the difference between the drill bit damage degree, the blade sharpness, the particle size standard deviation of the crushed rock and the rock breaking power obtained by the data twin model and the current drill bit state is calculated, and the difference is substituted into the abnormality evaluation formula to evaluate the abnormality of different parameters, wherein the abnormality evaluation formula is: , wherein is the difference of the i-th parameter, is the corresponding abnormal threshold of the i-th parameter.

[0021] Preferably, the future position hard rock hardness condition and shape condition based on the future position hard rock hardness condition and shape condition are analyzed, and the future position hard rock mining danger analysis includes the following specific steps:

[0022] S31, the future position rock layer inclination and rock mass blockiness are substituted into the hard rock morphology index evaluation formula to evaluate the influence of the rock layer inclination and rock mass blockiness on drilling, wherein the hard rock morphology index evaluation formula is: , wherein is the rock layer inclination, B is the rock mass blockiness, and is used to describe the typical size of the complete block in the rock mass, is the reference blockiness, and is the weight;

[0023] S32, the average height of the rock protrusion at the future position, the total projected area of the protrusion region and the average curvature of the protrusion part are substituted into the rock surface protrusion risk index evaluation formula to evaluate the damage risk of the local protrusion on the tool on the future drilling surface, wherein the rock surface protrusion risk index evaluation formula is: , wherein is the average height of the rock protrusion at the future position, is the reference height, is the total projected area of the rock protrusion region, is the total area of the observation surface, is the average curvature of the protrusion part, which is used to reflect the sharpness, and the higher the curvature, the more serious the local stress concentration, is a reference curvature, is a rock surface bulging risk weight, , and are weights;

[0024] S33, the future position rock uniaxial compressive strength, joint fissure density, hard rock form index and rock surface bulging risk index are substituted into the comprehensive danger index evaluation formula to evaluate the danger of the future drilling area, wherein the comprehensive danger index evaluation formula is: wherein, is the future position rock uniaxial compressive strength, is the reference strength, which is set according to the rated rock breaking capacity of the equipment, is the joint fissure density, which is used to represent the broken degree of the rock mass, the joint fissure density is the number of joints in unit volume, which represents the broken tendency and internal connectivity of the rock mass, , , and are weights. Preferably, the future mining health analysis based on the drill bit state anomaly analysis result and the future position hard rock mining danger analysis result comprises the following specific steps:

[0025] The anomaly degree and the comprehensive danger index are substituted into the future mining health index evaluation formula to evaluate the future mining health condition, wherein the future mining health index evaluation formula is: wherein, W is a tool state index, , and are weights, wherein the tool state index calculation formula is: wherein, and are weights.

[0026] Preferably, the mining health early warning based on the future mining health analysis result comprises the following specific steps:

[0027] The future mining health index is compared with a set threshold value to generate a graded early warning, when HI≥0.75, a green early warning is generated, the equipment and the rock mass are well adapted, and the existing operation parameters can be maintained to continue drilling, when 0.5≤HI<0.75, a yellow early warning is generated, prompting the operator to reduce the pushing speed and adjust the tool load, when HI<0.5, a red early warning is generated, the mining process is in a dangerous state, an alarm signal is immediately issued, and a shutdown inspection is performed, and the set threshold value can be modified according to the actual situation.

[0028] The hard rock mining equipment health diagnosis system based on digital twinning comprises the following modules:

[0029] A data acquisition module is configured to acquire corresponding equipment mining condition data and hard rock condition data through a data acquisition terminal.

[0030] A drill bit state evaluation module is configured to perform drill bit state anomaly analysis based on the equipment mining condition and the damage condition of the equipment mining drill bit.

[0031] A mining danger evaluation module is configured to perform future position hard rock mining danger analysis based on the hardness condition and the shape condition of the future position hard rock.

[0032] A mining health evaluation module is configured to perform future mining health analysis based on the drill bit state anomaly analysis result and the future position hard rock mining danger analysis result.

[0033] A health warning module is configured to perform mining health warning based on the future mining health analysis result.

[0034] An electronic device comprises a processor and a memory, wherein the memory stores a computer program that can be invoked by the processor.

[0035] The processor executes the hard rock mining equipment health diagnosis method based on digital twinning by invoking the computer program stored in the memory.

[0036] A computer readable storage medium stores instructions, and when the instructions are run on a computer, the computer executes the hard rock mining equipment health diagnosis method based on digital twinning.

[0037] Compared with the prior art, the present application has the following beneficial effects:

[0038] The present application acquires corresponding equipment mining condition data and hard rock condition data based on a data acquisition terminal, generates a data twinning model of the mining process through data twinning software, performs drill bit state anomaly analysis based on the equipment mining condition and the damage condition of the equipment mining drill bit, performs future position hard rock mining danger analysis based on the hardness condition and the shape condition of the future position hard rock, performs future mining health analysis based on the drill bit state anomaly analysis result and the future position hard rock mining danger analysis result, and performs mining health warning based on the future mining health analysis result, thereby realizing real-time health evaluation and hierarchical warning of the hard rock mining process and improving the mining safety and the intelligent level. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1Fig. 1 is a schematic diagram of the overall process of the hard rock mining equipment health diagnosis method based on digital twinning according to the present application;

[0040] Figure 2 Fig. 4 is a flowchart of future mining health index calculation according to the present application;

[0041] Figure 3 Fig. 5 is a schematic diagram of the overall framework of the hard rock mining equipment health diagnosis system based on digital twinning according to the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments.

[0043] Embodiment 1

[0044] Referring to Figures 1-2 An embodiment provided by the present application is a hard rock mining equipment health diagnosis method based on digital twinning, which comprises the following specific steps:

[0045] Step S1, obtaining corresponding equipment mining situation data and hard rock situation data based on a data acquisition terminal, and generating a data twinning model of the mining process through data twinning software;

[0046] Step S2, performing drill bit state anomaly analysis based on the equipment mining situation and the damage situation of the equipment mining drill bit;

[0047] Step S3, performing future position hard rock mining danger analysis based on the hardness situation and the shape situation of the future position hard rock;

[0048] Step S4, performing future mining health analysis based on the drill bit state anomaly analysis result and the future position hard rock mining danger analysis result;

[0049] Step S5, performing mining health early warning based on the future mining health analysis result.

[0050] In the present embodiment, it needs to be specifically explained that obtaining corresponding equipment mining situation data and hard rock situation data based on a data acquisition terminal, and generating a data twinning model of the mining process through data twinning software comprises the following specific steps:

[0051] S11, obtaining hard rock mining equipment mining situation and equipment data through a data acquisition terminal, including drill bit blade wear depth, wear area, drill bit rotation speed, cutting force and thrust force, etc., obtaining hard rock data through geological exploration means, using seismic wave reflection, acoustic wave transmission, geological radar, etc., to obtain parameters such as uniaxial compressive strength, rock hardness, fracture distribution and spatial structure in front;

[0052] S12, input the mining situation, equipment data and hard rock data into the digital twin software, combine the equipment physical model with the mining process simulation, generate a real-time updated mining process digital twin model, which is used to reflect the interactive state of the equipment and the rock mass synchronously, the equipment sub-model is used to describe the mechanical and energy transmission process of each component (drill bit, blade) of the mining equipment, the rock mass sub-model is used to describe the stress, crack propagation and fragmentation law of the rock mass in the drilling process, and the digital twin model can restore the mining process in the virtual space in real time and keep dynamic synchronization with the real equipment operation.

[0053] It needs to be specifically explained in this embodiment that the drill bit state abnormality analysis based on the equipment mining situation and the damage situation of the equipment mining drill bit includes the following specific steps:

[0054] S21, the wear depth and wear area of the drill bit blade of the mining equipment are substituted into the drill bit damage degree evaluation formula to evaluate the drill bit health condition, wherein the drill bit damage degree evaluation formula is: wherein D is the initial thickness of the blade, is the current blade thickness, A is the surface area of the blade tip, is the blade tip wear area, and are the depth weight coefficient and the area weight coefficient respectively, the greater the drill bit damage degree, the more serious the drill bit wear, the blade tip profile is obtained by laser scanning, optical measurement or high-precision camera, and the depth and area changes are calculated.

[0055] S22, the property parameters and operation parameters of the drill bit blade of the mining equipment are substituted into the blade sharpness evaluation formula to evaluate the cutting ability of the blade, wherein the blade sharpness evaluation formula is: wherein, is the cutting force, is the thrust force, n is the drill bit speed, is the blade geometric sharpness coefficient, which is obtained by the blade tip factory parameter, Y is the blade material hardness, which is obtained by the blade specification, wherein the property parameters of the drill bit blade of the mining equipment include the blade geometric sharpness coefficient and the blade material hardness, and the operation parameters include the cutting force, the thrust force and the drill bit speed, the decrease of sharpness will lead to the decrease of cutting efficiency and the acceleration of wear, the blade sharpness evaluation formula not only considers the working state, but also combines the material and structural characteristics of the blade itself;

[0056] S23, the particle size distribution of the broken rock of the mining equipment is counted, and the particle size standard deviation of the broken rock is calculated, the standard deviation of the rock particle size distribution is used to measure the crushing uniformity, the greater the standard deviation, the worse the mining uniformity;

[0057] S24, the bit torque and angular velocity of the mining equipment are substituted into the rock breaking power evaluation formula to evaluate the unit rock volume breaking power, wherein the rock breaking power evaluation formula is: wherein T is the bit torque, v is the angular velocity, is the volume of rock broken per unit time, and the greater the power indicates that the equipment load is higher, the tool efficiency is low, or the bit is severely worn;

[0058] S25, the difference between the bit damage degree, blade sharpness, rock breaking particle size standard deviation and rock breaking power obtained by the data twin model and the current bit state is calculated, and the difference is substituted into the abnormality evaluation formula to evaluate the abnormality of different parameters, wherein the abnormality evaluation formula is: wherein, is the difference of the i-th parameter, is the corresponding abnormal threshold of the i-th parameter, and the smaller the abnormality indicates that the equipment operation is highly consistent with the twin model.

[0059] In this embodiment, it needs to be specifically explained that the future position hard rock hardness and shape are analyzed for future position hard rock mining risk, including the following specific steps:

[0060] S31, the future position rock layer inclination and rock mass blockiness are substituted into the hard rock morphology index evaluation formula to evaluate the influence of the rock layer inclination and rock mass blockiness on drilling, wherein the hard rock morphology index evaluation formula is: wherein, is the rock layer inclination, the rock layer inclination affects the bit stress direction and cutting stability, large inclination causes eccentric load or stripping, the rock layer inclination is obtained by stratum profile and inclination fitting of the advanced geological radar, B is the rock mass blockiness, which is used to describe the typical size of the complete block in the rock mass, and large blockiness indicates that complete large blocks appear and require higher load to break, which is easy to cause instantaneous overload or collapse, is the reference blockiness, and the reference blockiness is the typical breaking scale, and is the weight, represents the normal shear component and friction change caused by the inclination;

[0061] S32, the average height of the rock protrusion at the future position, the total projected area of the protrusion region, and the average curvature of the protrusion part are substituted into the rock surface protrusion risk index evaluation formula to evaluate the damage risk of the tool by the local protrusion on the future drilling surface, wherein the rock surface protrusion risk index evaluation formula is: wherein, is the average height of the rock protrusion at the future position, is the reference height, which is the depth of the blade cutting, is the total projected area of the rock protrusion region, is the total area of the observation surface, is the average curvature of the convex part, which is used to reflect the sharpness, and the higher the curvature, the more serious the local stress concentration, is the reference curvature, the reference curvature is the curvature of the tool nose radius, for the same feed speed, the higher the convexity, the greater the relative displacement of the blade, and the stronger the impact, is the rock surface convex risk weight, , and are weights, and the total projected area of the rock convex region reflects the coverage of the problem area;

[0062] S33, the uniaxial compressive strength of the rock at the future position, the joint fissure density, the hard rock form index and the rock surface convex risk index are substituted into the comprehensive risk index evaluation formula to evaluate the risk of the future drilling area, wherein the comprehensive risk index evaluation formula is: wherein, is the uniaxial compressive strength of the rock at the future position, is the reference strength, which is set according to the rated rock breaking capacity of the equipment, is the joint fissure density, which is used to represent the fragmentation degree of the rock mass, and the joint fissure density is the number of joints per unit volume, which represents the fragmentation tendency and internal connectivity of the rock mass, and the rock mass with more fissures is usually more brittle and prone to sticking or local block loosening, , , and are weights, and when is too high, it indicates that the strength of the rock mass exceeds the design capacity of the equipment, and the drilling risk increases, when the joint fissure density is large, the rock mass has many internal fissures, which will cause the drill bit to stick or deviate, and when the rock layer inclination is too large and the block size is abnormal, the drill bit will be unevenly stressed, which will cause collapse or tool breakage.

[0063] In the present embodiment, it needs to be specifically described that the future mining health analysis based on the drill bit state abnormality analysis result and the future position hard rock mining risk analysis result includes the following specific steps:

[0064] S41, the abnormality degree and the comprehensive risk index are substituted into the future mining health index evaluation formula to evaluate the future mining health condition, wherein the future mining health index evaluation formula is: wherein, W is a tool state index, , and are weights, wherein the tool state index calculation formula is: wherein, and The weight is the abnormality index, which represents the difference between the current equipment and the twin model, and represents the abnormality in the interaction between the tool, the equipment and the rock mass. The comprehensive risk index represents the strength, structure and morphology of the hard rock area in front of the drill, and the tool state index reflects the remaining life and efficiency of the tool based on the damage degree of the drill bit and the sharpness of the blade.

[0065] It is particularly pointed out in the present embodiment that the mining health early warning based on the future mining health analysis result includes the following specific steps:

[0066] The future mining health index is compared with the set threshold value to generate a graded early warning. When HI≥0.75, a green early warning is generated, indicating that the equipment and rock mass are well adapted. When the health index is high, the fragmentation uniformity is good, and the tool is sharp, the existing operation parameters can be maintained to continue drilling. When 0.5≤HI<0.75, a yellow early warning is generated, prompting the operator to reduce the pushing speed and adjust the tool load. When HI<0.5, a red early warning is generated, indicating that the mining process is in a dangerous state, and an alarm signal is immediately issued to stop the machine for inspection. The set threshold value can be modified according to the actual situation.

[0067] It is pointed out here that the value of the various set parameters in the present embodiment is obtained by: obtaining representative historical equipment data during hard rock mining, obtaining hard rock data during hard rock mining, hiring experts to manually judge whether the mining safety meets the requirements, and inputting the historical data into the calculation results and judgment results of each step in the present embodiment into the fitting software to output the values of the various set parameters with the highest judgment accuracy.

[0068] The present embodiment has the following advantages over the prior art:

[0069] The present application obtains corresponding equipment mining data and hard rock data based on the data acquisition terminal, generates a data twin model of the mining process through data twin software, analyzes tool state abnormalities based on the equipment mining situation and the damage of the equipment mining drill bit, analyzes the mining risk of the future location hard rock based on the hardness and shape of the future location hard rock, analyzes the future mining health based on the tool state abnormality analysis result and the future location hard rock mining risk analysis result, and performs mining health early warning based on the future mining health analysis result. The present application realizes real-time health evaluation and graded early warning of the hard rock mining process, and improves the mining safety and intelligent level.

[0070] Embodiment 2

[0071] As Figure 3As shown, the hard rock mining equipment health diagnosis system based on digital twinning is realized based on the above-mentioned hard rock mining equipment health diagnosis method based on digital twinning, and specifically includes a data acquisition module, a drill bit state evaluation module, a mining danger evaluation module, a mining health evaluation module, and a health warning module. The data acquisition module is used to acquire corresponding equipment mining condition data and hard rock condition data through a data acquisition terminal. The drill bit state evaluation module is used to analyze drill bit state abnormalities through equipment mining conditions and damage conditions of equipment mining drill bits. The mining danger evaluation module is used to analyze future location hard rock mining danger through hardness conditions and shape conditions of future location hard rocks. The mining health evaluation module is used to analyze future mining health through drill bit state abnormality analysis results and future location hard rock mining danger analysis results. The health warning module is used to warn mining health through future mining health analysis results.

[0072] Embodiment 3

[0073] The embodiment provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0074] The processor executes the above-mentioned hard rock mining equipment health diagnosis method based on digital twinning by calling the computer program stored in the memory.

[0075] The electronic device can have great differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to realize the hard rock mining equipment health diagnosis method based on digital twinning provided by the above-mentioned hard rock mining equipment health diagnosis method based on digital twinning. The electronic device can also include other components for realizing device functions, for example, the electronic device can also have a wired or wireless network interface and an input and output interface, etc., so as to input and output data. This embodiment will not be repeated here.

[0076] Embodiment 4

[0077] The embodiment provides a computer readable storage medium, which stores an erasable computer program;

[0078] When the computer program runs on the computer device, the computer device executes the above-mentioned hard rock mining equipment health diagnosis method based on digital twinning.

[0079] For example, the computer readable storage medium can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.

[0080] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are entirely or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired network or / and a wireless network. The computer readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing one or more available medium sets. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

Claims

1. A health diagnosis method for hard rock mining equipment based on digital twins, characterized in that, It includes the following specific steps: Step S1: Obtain the corresponding equipment mining status data and hard rock condition data based on the data acquisition terminal, and generate a data twin model of the mining process through data twin software; Step S2: Analyze the abnormal state of the drill bit based on the equipment mining conditions and the damage status of the drill bit. Step S3: Based on the hardness and shape of the hard rock at the future location, conduct a risk analysis of hard rock mining at the future location, including the following specific steps: Substitute the dip angle and block size of the rock strata at the future location into the hard rock morphology index evaluation formula to evaluate the impact of the dip angle and block size on drilling. The hard rock morphology index evaluation formula is as follows: ,in, B represents the dip angle of the rock strata, and B represents the block size of the rock mass. For reference block size, and Using the average height of future rock protrusions, the total projected area of ​​the protrusion region, and the average curvature of the protrusion as weights, the rock protrusion risk index assessment formula is used to evaluate the risk of local protrusions on the drilling surface damaging the cutting tool in the future drilling process. The rock protrusion risk index assessment formula is as follows: ,in, The average height of the rock protrusion at the future location. For reference height, This represents the total projected area of ​​the rock protrusion. The total area of ​​the observation surface, This represents the average curvature of the protruding portion. Higher curvature leads to more severe local stress concentration. For reference curvature, For the risk weight of rock surface protrusion, , and As weights, the future location's uniaxial compressive strength, joint and fracture density, hard rock morphology index, and rock surface protrusion risk index are substituted into the comprehensive risk index assessment formula to evaluate the risk of the future drilling area. The comprehensive risk index assessment formula is as follows: ,in, For the future location of the rock uniaxial compressive strength, As the reference strength, For joint and fracture density, , , and As weight; Step S4: Based on the results of the drill bit condition anomaly analysis and the results of the future hard rock mining hazard analysis, conduct a future mining health analysis, including the following specific steps: Substitute the anomaly degree and comprehensive hazard index into the future mining health index assessment formula to evaluate the future mining health status. The future mining health index assessment formula is as follows: Where W is the tool condition index. , and The weights are given by the tool condition index, which is calculated using the following formula: ,in, and As weight; Step S5: Conduct mining health early warning based on the results of future mining health analysis.

2. The method for health diagnosis of hard rock mining equipment based on digital twins as described in claim 1, characterized in that, The process of acquiring corresponding equipment mining data and hard rock condition data based on the data acquisition terminal, and generating a data twin model of the mining process using data twin software, includes the following specific steps: S11. Obtain mining status and equipment data of hard rock mining equipment through data acquisition terminals, and obtain hard rock data through geological exploration methods; S12. Input the mining situation, equipment data and hard rock data into the digital twin software, and combine the equipment physical model with the mining process simulation to generate a real-time updated digital twin model of the mining process.

3. The method for health diagnosis of hard rock mining equipment based on digital twins as described in claim 2, characterized in that, The abnormal drill bit condition analysis based on the equipment mining situation and the damage status of the drill bit includes the following specific steps: S21. Substitute the wear depth and wear area of ​​the drill bit cutting edge of the mining equipment into the drill bit damage assessment formula to evaluate the health status of the drill bit. The drill bit damage assessment formula is as follows: Where D is the initial thickness of the blade. Where A is the current blade thickness and A is the blade tip surface area. This represents the area of ​​wear on the blade tip. and These are the depth weighting coefficient and the area weighting coefficient, respectively. S22. Substitute the property parameters and operating parameters of the drill bit blades of the mining equipment into the blade sharpness evaluation formula to evaluate the cutting ability of the blades. The blade sharpness evaluation formula is as follows: ,in, For cutting force, The thrust is n, and the drill bit rotation speed is n. is the geometric sharpness coefficient of the blade, and Y is the hardness of the blade material; S23. Statistically analyze the particle size distribution of the rock crushed by mining equipment and calculate the standard deviation of the particle size of the crushed rock; S24. Substitute the drill bit torque and angular velocity of the mining equipment into the rock-breaking power evaluation formula to evaluate the rock-breaking power per unit volume. The rock-breaking power evaluation formula is as follows: Where T is the drill bit torque and v is the angular velocity. The volume of rock broken per unit time; S25. Calculate the differences between the drill bit damage level, blade sharpness, standard deviation of rock particle size, and rock-breaking power obtained from the data twin model and the current drill bit condition. Substitute these differences into the anomaly assessment formula to evaluate the anomalies of different parameters. The rock-breaking power assessment formula is as follows: ,in, Let be the difference of the i-th parameter. Let be the abnormal threshold corresponding to the i-th parameter.

4. The method for health diagnosis of hard rock mining equipment based on digital twins as described in claim 3, characterized in that, The mining health early warning based on future mining health analysis results includes the following specific steps: The future mining health index is compared with the set threshold to generate graded early warnings. When HI≥0.75, a green warning is generated, indicating that the equipment and rock mass are well adapted. When the health index is high, the crushing uniformity is good, and the cutting tools are sharp, drilling can continue with the existing operating parameters. When 0.5≤HI<0.75, a yellow warning is generated, prompting the operator to reduce the advance speed and adjust the cutting tool load. When HI<0.5, a red warning is generated, indicating that the mining process is in a dangerous state, and an alarm signal is immediately issued to stop the machine for inspection.

5. A health diagnosis system for hard rock mining equipment based on digital twins, implemented based on the health diagnosis method for hard rock mining equipment based on digital twins as described in any one of claims 1-4, characterized in that, Specifically, it includes: The data acquisition module is used to acquire corresponding equipment mining status data and hard rock condition data through the data acquisition terminal; The drill bit condition assessment module is used to analyze abnormal drill bit conditions based on the equipment's mining status and the damage to the drill bit. The mining hazard assessment module is used to analyze the mining hazard of hard rock at a future location based on the hardness and shape of the hard rock at that location. The mining health assessment module is used to conduct future mining health analysis based on the results of drill bit condition anomaly analysis and the results of future hard rock mining hazard analysis. The health early warning module is used to provide early warnings about mining health based on future mining health analysis results.

6. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor is characterized in that it executes the digital twin-based health diagnosis method for hard rock mining equipment as described in any one of claims 1-4 by calling a computer program stored in the memory.

7. A computer-readable storage medium, characterized in that, The device stores instructions that, when executed on a computer, cause the computer to perform the digital twin-based health diagnosis method for hard rock mining equipment as described in any one of claims 1-4.

Images