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

Abstract

The invention discloses a hard rock mining equipment health diagnosis system and method based on digital twinning, and belongs to the technical field of quality detection.According to the hard rock mining equipment health diagnosis system and method based on digital twinning, corresponding equipment mining condition data and hard rock condition data are obtained based on a data obtaining terminal, and a mining process is generated through data twinning software to obtain a data twinning model; performing drill bit state abnormity analysis based on the equipment mining condition and the damage condition of the equipment mining drill bit, and performing future position hard rock mining risk analysis based on the hardness condition and the shape condition of the future position hard rock; future mining health analysis is carried out based on the drill bit state abnormity analysis result and the future position hard rock mining risk analysis result, mining health early warning is carried out based on the future mining health analysis result, real-time health assessment and graded early warning in the hard rock mining process are achieved, 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 position, 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 To achieve the above purpose, the application provides the following technical scheme: The hard rock mining equipment health diagnosis method based on digital twinning includes the following specific steps: 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; Step S2, performing drill bit state anomaly analysis based on the equipment mining situation and the damage situation of the equipment mining drill bit; Step S3, performing future position hard rock mining risk analysis based on the hardness and shape of the future position hard rock; Step S4, performing future mining health analysis based on the drill bit state anomaly analysis result and the future position hard rock mining risk analysis result; Step S5, performing mining health early warning based on the future mining health analysis result.

[0005] Preferably, the data acquisition terminal obtains the corresponding equipment mining situation data and hard rock situation data, and generates a mining process data twin model through data twin software, including the following specific steps: S11, obtaining the hard rock mining equipment mining situation and equipment data through the data acquisition terminal, including the wear depth, wear area, drill bit speed, cutting force and thrust force of the drill bit, obtaining the hard rock data through geological exploration means, using seismic wave reflection, acoustic wave transmission, geological radar and other methods to obtain the uniaxial compressive strength, rock hardness, fracture distribution and spatial structure of the front; S12, inputting the mining situation, equipment data and hard rock data into the digital twin software, combining the equipment physical model and the mining process simulation to generate a real-time updated mining process digital twin model for synchronously reflecting 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 fragmentation law of the rock mass in the drilling process.

[0006] Preferably, the drill bit state anomaly analysis based on the equipment mining situation and the damage situation of the equipment mining drill bit includes the following specific steps: S21, substituting the wear depth and wear area of the drill bit of the mining equipment 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; S22, substituting the property parameters and operating parameters of the drill bit of the mining equipment 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 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; S23, statistics the particle size distribution of the broken rock of the mining equipment, and calculates the particle size standard deviation of the broken rock, the standard deviation of the rock particle size distribution is used to measure the crushing uniformity, and the greater the standard deviation, the worse the mining uniformity; 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 rock broken per unit time, and the greater the power indicates that the equipment load is higher, the tool efficiency is low, or the drill bit is severely worn; S25, the drill bit damage degree, blade sharpness, rock breaking particle size standard deviation, and rock breaking power obtained by the data twin model are calculated with the current drill bit state to obtain a difference value, and the difference value is substituted into the abnormality degree evaluation formula to evaluate the abnormality of different parameters, wherein the abnormality degree evaluation formula is: wherein, is the difference value of the i-th parameter, is the corresponding abnormal threshold value of the i-th parameter.

[0007] Preferably, the future location hard rock hardness condition and shape condition based future location hard rock mining danger analysis comprises the following specific steps: S31, the future location 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 are weights; S32, the rock protrusion average height, total projected area of the protrusion region, and average value of the curvature of the protrusion part of the future location are substituted into the rock surface protrusion risk index evaluation formula to evaluate the damage risk of the local protrusion on the tool during future drilling, wherein the rock surface protrusion risk index evaluation formula is: wherein, is the rock protrusion average height of the future location, 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 value of the 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 the reference curvature, is the rock surface protrusion risk weight, , and are weights; S33, the future position rock uniaxial compressive strength, joint fracture density, hard rock form index and 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: , , , , , , , are weights. Preferably, the future mining health analysis based on the drill bit state abnormality analysis result and the future position hard rock mining risk analysis result comprises the following specific steps: The abnormality degree and the comprehensive risk index are substituted into the future mining health index evaluation formula to evaluate the future mining health situation, wherein the future mining health index evaluation formula is: , , , are weights, wherein the drill bit state index calculation formula is: , , are weights.

[0008] Preferably, the mining health early warning based on the future mining health analysis result comprises the following specific steps: 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, 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 advance speed and adjust the cutter 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.

[0009] 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 comprises: A data acquisition module is configured to acquire corresponding equipment mining situation data and hard rock situation data through a data acquisition terminal. A drill bit state evaluation module is configured to perform drill bit state abnormality analysis based on the equipment mining situation and the damage situation of the equipment mining drill bit. The mining risk assessment module is configured to analyze the mining risk of the hard rock at the future position by the hardness condition and the shape condition of the hard rock at the future position. The mining health assessment module is configured to analyze the future mining health by the abnormal analysis result of the drill bit state and the analysis result of the mining risk of the hard rock at the future position. The health early warning module is configured to perform the mining health early warning by the analysis result of the future mining health.

[0010] An electronic device comprises a processor and a memory, wherein the memory stores a computer program that can be invoked by the processor. The processor performs the hard rock mining equipment health diagnosis method based on digital twinning by invoking the computer program stored in the memory.

[0011] A computer readable storage medium stores instructions, which, when executed on a computer, cause the computer to perform the hard rock mining equipment health diagnosis method based on digital twinning.

[0012] Compared with the prior art, the beneficial effects of the present application are: The present application obtains the corresponding equipment mining condition data and hard rock condition data based on the data acquisition terminal, generates a data twin model of the mining process by a data twin software, analyzes the abnormal state of the drill bit based on the equipment mining condition and the damage condition of the drill bit for the equipment mining, analyzes the mining risk of the hard rock at the future position based on the hardness condition and the shape condition of the hard rock at the future position, analyzes the future mining health based on the abnormal analysis result of the drill bit state and the analysis result of the mining risk of the hard rock at the future position, and performs the mining health early warning based on the analysis result of the future mining health, thereby realizing the real-time health evaluation and hierarchical early warning of the hard rock mining process, and improving the mining safety and the intelligent level. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 The present application is a whole flowchart of the hard rock mining equipment health diagnosis method based on digital twinning. Figure 2 The present application is a flowchart of the future mining health index calculation. Figure 3 The present application is a whole framework diagram of the hard rock mining equipment health diagnosis system based on digital twinning. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present application will be described clearly and completely 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.

[0015] Embodiment 1

[0016] Referring to Figures 1-2 In an embodiment, the application provides a hard rock mining equipment health diagnosis method based on digital twinning, which comprises the following specific steps: 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; Step S2, analyzing the drill bit state anomaly based on the equipment mining situation and the damage situation of the equipment mining drill bit; Step S3, analyzing the hard rock mining danger at the future position based on the hardness and shape of the hard rock at the future position; 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; Step S5, performing mining health early warning based on the future mining health analysis result.

[0017] In this embodiment, it needs to be specifically pointed out that 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 comprises the following specific steps: S11, obtaining the hard rock mining equipment mining situation and equipment data through a data acquisition terminal, including the wear depth, wear area, drill bit speed, cutting force and thrust force of the drill bit blade, obtaining the hard rock data through geological exploration means, using seismic wave reflection, acoustic wave transmission, geological radar and other methods to obtain the uniaxial compressive strength, rock hardness, fracture distribution and spatial structure of the front, and other parameters; S12, inputting the mining situation, equipment data and hard rock data into the digital twinning software, combining the equipment physical model and the mining process simulation to generate a real-time updated mining process digital twinning model, which is used to reflect the interactive state of the equipment and the rock mass in synchronization, 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, fracture propagation and crushing law of the rock mass in the drilling process, and the digital twinning model can restore the mining process in real time in the virtual space and keep dynamic synchronization with the real equipment operation.

[0018] In this embodiment, it needs to be specifically pointed out that the drill bit state anomaly analysis based on the equipment mining situation and the damage situation of the equipment mining drill bit comprises the following specific steps: S21, substituting the wear depth and wear area of the drill bit blade of the mining equipment 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 blade tip surface area, is the blade tip wear area, and respectively, the depth weight coefficient and the area weight coefficient, the greater the drill bit damage degree indicates 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.

[0019] S22, the property parameters and operation parameters of the drill bit 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 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 sharpness decrease will lead to the cutting efficiency decrease and the wear acceleration, the blade sharpness evaluation formula not only considers the working state, but also combines the material and structural characteristics of the blade itself; 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, and the greater the standard deviation, the worse the mining uniformity; 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 unit rock volume broken per unit time, and the greater the power indicates the higher the equipment load, and the tool efficiency is low or the drill bit is seriously worn; S25, the drill bit damage degree, the blade sharpness, the particle size standard deviation of the broken rock and the rock breaking power obtained by the data twin model are 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.

[0020] It needs to be specifically explained in the embodiment that the future position hard rock hardness and shape are analyzed to analyze the future position hard rock mining risk, which includes the following specific steps: S31. Substitute the future location rock strata dip angle and rock mass block size into the hard rock morphology index evaluation formula to assess the impact of rock strata dip angle and rock mass block size on drilling. The hard rock morphology index evaluation formula is as follows: ,in, The rock strata dip angle affects the direction of force on the drill bit and cutting stability. A large dip angle can cause eccentric loading or stripping. The rock strata dip angle is obtained by fitting the dip angle of the stratigraphic profile and advanced ground-penetrating radar. B is the rock mass block size, which describes the typical size of intact blocks in the rock mass. A large block size indicates that it is necessary to load more heavily to break up the intact large blocks, which can easily lead to instantaneous overload or collapse. The reference size is a typical fragmentation scale. and As weight, This represents the changes in normal shear composition and friction caused by the tilt angle; S32. Substitute the average height of the rock protrusion at the future location, the total projected area of ​​the protrusion region, and the average curvature of the protrusion into the rock protrusion risk index assessment formula to evaluate the risk of local protrusions on the drilling surface to damage the cutting tool. The rock protrusion risk index assessment formula is as follows: ,in, The average height of the rock protrusion at the future location. This is a reference height, which represents the blade's cutting depth. 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, used to reflect the sharpness; a higher curvature leads to more severe local stress concentration. The reference curvature is the curvature of the tool tip radius. For the same feed rate, the higher the bulge, the greater the relative displacement of the insert and the stronger the impact. For the risk weight of rock surface protrusion, , and As a weight, the proportion of the total projected area of ​​the rock protrusion area reflects the degree of coverage of the problematic area; S33. Substitute the future location's uniaxial compressive strength, joint and fracture density, hard rock morphology index, and rock surface protrusion risk index 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, The benchmark strength is set based on the equipment's rated rock-breaking capacity. Joint and fracture density is used to indicate the degree of rock mass fracturing. It is the number of joints per unit volume, representing the rock mass's tendency to fracture and its internal connectivity. Rock masses with more fractures are generally more brittle and prone to drill bit jamming or localized block loosening. , , and As weight, when When the value is too high, it indicates that the rock mass strength exceeds the equipment design capacity, increasing the drilling risk. When the joint and fissure density is large, there are many internal fissures in the rock mass, which can cause the drill bit to get stuck or deviate. When the rock strata dip angle is too large or the block size is abnormal, the drill bit is subjected to uneven force, which may cause collapse or tool breakage.

[0021] In this embodiment, it should be specifically explained that the future mining health analysis based on the results of drill bit condition anomaly analysis and the results of future hard rock mining hazard analysis includes the following specific steps: S41. Substitute the anomaly degree and comprehensive risk 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 The anomaly index, with weights, represents the degree of difference between the current equipment and the twin model, indicating the degree of anomaly in the interaction process between the cutting tool, equipment, and rock mass. The comprehensive risk index represents the risk of drilling to the strength, structure, and morphology of the hard rock area ahead. The cutting tool condition index is based on the degree of drill bit damage and the sharpness of the cutting edge, reflecting the remaining life and efficiency of the cutting tool.

[0022] In this embodiment, it should be specifically explained that the mining health early warning based on the 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 good equipment and rock mass compatibility. 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 operators 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. The set thresholds can be modified according to the actual situation.

[0023] It should be noted that the value mode of the various set parameters in the embodiment is: obtaining representative historical hard rock mining process equipment data, obtaining hard rock data in the hard rock mining process, 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 embodiment into the fitting software to output various set parameters with the highest judgment accuracy. The benefits of the embodiment over the prior art are: The application obtains corresponding equipment mining situation data and hard rock situation data based on a data acquisition terminal, generates a mining process data twin model through data twin software, analyzes drill bit state abnormalities based on equipment mining situation and drill bit damage in equipment mining, analyzes future location hard rock mining danger based on the hardness and shape of the future location hard rock, analyzes future mining health based on the drill bit state abnormality analysis result and the future location hard rock mining danger analysis result, and performs mining health early warning based on the future mining health analysis result, thereby realizing real-time health evaluation and hierarchical early warning of the hard rock mining process, and improving the mining safety and intelligent level.

[0024] Embodiment 2

[0025] As shown in Figure 3 The hard rock mining equipment health diagnosis system based on digital twinning is implemented 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 early warning module. The data acquisition module is used to obtain corresponding equipment mining situation data and hard rock situation data through a data acquisition terminal. The drill bit state evaluation module is used to analyze drill bit state abnormalities through equipment mining situation and drill bit damage in equipment mining. The mining danger evaluation module is used to analyze future location hard rock mining danger through the hardness and shape of the future location hard rock. The mining health evaluation module is used to analyze future mining health through the drill bit state abnormality analysis result and the future location hard rock mining danger analysis result. The health early warning module is used to perform mining health early warning through the future mining health analysis result.

[0026] Embodiment 3

[0027] The embodiment provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor; 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.

[0028] The electronic device can have a large difference due to configuration or performance, 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 loaded and executed by the processor to implement the digital twin-based hard rock mining equipment health diagnosis method provided by the above-mentioned embodiment of the digital twin-based hard rock mining equipment health diagnosis method. The electronic device can also include other components for implementing device functions, for example, the electronic device can also have a wired or wireless network interface and an input and output interface, and the like, so as to input and output data. This embodiment will not be described here.

[0029] Embodiment 4

[0030] The embodiment provides a computer readable storage medium, which stores an erasable computer program. When the computer program runs on the computer device, the computer device executes the above-mentioned digital twin-based hard rock mining equipment health diagnosis method.

[0031] For example, the computer readable storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk and an optical data storage device, etc.

[0032] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part 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 wholly 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 computer-readable storage medium, 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, or the like including a set of one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a 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: Conduct a future mining health analysis based on the results of the drill bit condition anomaly analysis and the results of the future location hard rock mining hazard analysis. 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 future mining health analysis based on the results of drill bit condition anomaly analysis and the results of future hard rock mining hazard analysis includes the following specific steps: The anomaly degree and comprehensive risk index are substituted 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.

5. The method for health diagnosis of hard rock mining equipment based on digital twins as described in claim 4, 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.

6. 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 1-5, 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.

7. 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 1-5 by calling a computer program stored in the memory.

8. 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 1-5.

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