High-pressure jet intelligent mining self-adaptive monitoring control system

Abstract

The invention discloses a high-pressure jet flow intelligent mining self-adaptive monitoring control system which comprises a jet flow rock breaking dynamic monitoring module, a rock mass analysis and data processing module, a neural network deep learning module and a mining machine controller. The jet flow rock breaking dynamic monitoring module is used for obtaining jet flow parameters, calculating jet flow axial reverse thrust, obtaining the three-dimensional shape of a mining face and obtaining cutting and crack shapes. Wherein the three-dimensional shape of the mining face at least comprises cutting depth information; the rock mass analysis and data processing module is used for acquiring three-dimensional coordinates of the mining machine in a working environment, calculating rock mass confining pressure and acquiring rock mass characteristics; the neural network deep learning module establishes a jet cutting process parameter and mining process parameter intelligent model; the mining machine controller is used for generating a mining machine control instruction. According to the invention, the high-pressure jet mining equipment can be safely and efficiently controlled to carry out self-adaptive cutting on the ore / rock mass.

Description

Technical Field

[0001] The invention relates to underground operations, and in particular to a high-pressure jet intelligent mining adaptive monitoring and control system. Background Art

[0002] my country's underground engineering technologies such as tunnels, urban underground spaces, and energy storage underground caverns are developing rapidly, and the scale of projects and excavation depths are growing rapidly. Similar to deep solid resource mining, they are faced with problems such as serious over-excavation and under-excavation, severe surrounding rock damage, and frequent disasters such as rock bursts. Traditional rock construction methods (drilling and blasting, cantilever mining, and TBM) have serious tool wear, large vibrations in drilling and blasting, and severe surrounding rock damage, especially having a great impact on urban structures and residents, low mining efficiency, high construction costs, and high risks. Therefore, the use of high-pressure jet technology has gradually become the mainstream development trend in the field of mining technology. However, the current high-pressure jet mining equipment has a low degree of intelligence, and cannot adaptively adjust equipment parameters according to different rock mass data, and cannot timely evaluate the mining working environment and safety risk conditions. Summary of the invention

[0003] In view of this, the object of the present invention is to provide a high-pressure jet intelligent mining adaptive monitoring and control system, which can achieve safe and efficient control of high-pressure jet mining equipment to perform adaptive cutting of ore / rock.

[0004] A high-pressure jet intelligent mining adaptive monitoring and control system in the present invention includes a jet rock breaking dynamic monitoring module, a rock mass analysis and data processing module, a neural network deep learning module and a mining machine controller;

[0005] The jet rock breaking dynamic monitoring module is used to: obtain jet parameters and calculate the jet axial reverse thrust, obtain the three-dimensional morphology of the mining surface, and obtain the cutting and crack morphology; wherein the three-dimensional morphology of the mining surface at least includes cutting depth information;

[0006] The rock mass analysis and data processing module is used to obtain the three-dimensional coordinates of the mining machine in the working environment, calculate the rock mass confining pressure and obtain the rock mass characteristics;

[0007] The neural network deep learning module is used to:

[0008] Collect and store historical and real-time data on jet parameters, jet axial reverse thrust, three-dimensional morphology of the mining face, and cutting and crack morphology, and establish a dynamic evaluation model for jet cutting status and effect;

[0009] Collect and store historical and real-time monitoring data of the three-dimensional coordinates of the mining machine in the working environment, rock mass confining pressure and rock mass characteristics, and establish a dynamic evaluation model for safety risks and rock mass data;

[0010] Based on the jet parameters, cutting depth information, and historical data and real-time monitoring data of rock mass characteristics, a cutting effect evaluation model for different lithologies is established;

[0011] Based on the dynamic evaluation model of jet cutting status and effect, the dynamic evaluation model of safety risk and rock mass data, and the cutting effect evaluation model, with the crushing block size, cutting depth, and surrounding rock deformation as the objective function, a jet cutting process parameter database is established, and an intelligent model of jet cutting process parameters and mining process parameters is established;

[0012] The mining machine controller is used to: determine the optimal position and posture of the mining machine and the jet cutting mechanism, the optimal jet parameters based on the real-time monitoring data of the jet rock breaking dynamic monitoring module and the rock analysis and data processing module, combined with the jet cutting process parameters and the mining process parameter intelligent model, and generate mining machine control instructions.

[0013] Furthermore, the jet rock breaking dynamic monitoring module includes a jet axial reverse thrust monitoring component, a mining face laser ranging monitoring component and an acoustic emission rock fracture dynamic monitoring component;

[0014] The jet axial reverse thrust monitoring component is used to obtain jet parameters and calculate the jet axial reverse thrust; the mining face laser ranging monitoring component is used to obtain the three-dimensional morphology of the mining face; the acoustic emission rock fracture dynamic monitoring component is used to obtain the cutting and crack morphology.

[0015] Furthermore, the jet parameters include: jet mass, jet velocity, jet momentum, jet area, incoming flow mass, incoming flow velocity, incoming flow momentum, the angle formed by the jet and the impacting object, jet density and jet unit flow rate;

[0016] The jet axial reverse thrust monitoring component calculates the jet axial reverse thrust F2 in the following manner:

[0017] When there is no target body in the jet, the impact thrust F0: = F0 = m0V0-mV+(P0-P)A0;

[0018] When the jet hits the target, the impact thrust F1: F1 = ρQV0sin 2 θ;

[0019] When θ=90°:F1=ρQV0;

[0020] When θ=180°:F1=2ρQV0;

[0021] Then the jet axial reverse thrust F2 is: F2 = F0 + F1;

[0022] Among them: m0 represents the jet mass, V0 represents the jet velocity, P0 represents the jet momentum, A0 represents the jet area, m represents the incoming flow mass, V represents the incoming flow velocity, P represents the incoming flow momentum, θ represents the angle formed by the jet and the impacting object, ρ represents the jet density, and Q represents the unit flow rate of the jet.

[0023] Furthermore, the mining face laser ranging monitoring component includes a three-dimensional scanning device and a laser ranging device. The three-dimensional scanning device is used to scan the three-dimensional morphology of the face, and the laser ranging device is used to detect the surface morphology of the mining working face, the distance between the jet nozzle and the target body, the cutting depth information and the cutting seam width information, so as to obtain the three-dimensional morphology of the mining face.

[0024] Furthermore, the acoustic emission rock fracture dynamic monitoring component determines the cutting depth information of the rock by monitoring the change of the acoustic signal, and reflects the whole process of rock jet cutting cracks in real time, so as to obtain the cutting and crack morphology.

[0025] Furthermore, the rock mass analysis and data processing module includes a rock mass excavation face three-dimensional positioning component, a tunnel face equivalent confining pressure inversion component, and a rock mass characteristic identification component;

[0026] The mining face three-dimensional positioning component is used to obtain the three-dimensional coordinates of the mining machine in the working environment; the tunnel face equivalent confining pressure inversion component is used to calculate the rock mass confining pressure; and the rock mass property identification component is used to obtain rock mass properties.

[0027] Furthermore, the process of calculating the rock mass confining pressure by the equivalent confining pressure inversion component of the tunnel face includes:

[0028] A mathematical model of rock mass confining pressure, cutting depth and jet parameters is established through the neural network deep learning module. The jet axial reverse thrust obtained by the jet axial reverse thrust monitoring component, the cutting depth information obtained by the mining face laser ranging monitoring component and the stress change obtained by the microseismic signal processing results are combined with the jet parameters to inversely learn and correct the rock mass confining pressure of the face. Based on the inversely calculated rock mass confining pressure of the face, the crack initiation stress and damage stress are qualitatively evaluated, and a map of the relationship between punching depth and confining pressure is established based on indoor simulation experiments and field tests to generate the equivalent stress diagram of the face.

[0029] Furthermore, the mathematical model of rock mass confining pressure, cutting depth and jet parameters is established through the neural network deep learning module, and the jet axial reverse thrust obtained by the jet axial reverse thrust monitoring component, the cutting depth information obtained by the mining face laser ranging monitoring component and the stress change obtained by the microseismic signal processing result are combined with the jet parameters to inversely learn and correct the rock mass confining pressure of the face, and the inverse learning and correction are performed through the following mathematical model:

[0030]

[0031] Where: H is the cutting depth under pressure, H0 is the cutting depth without confining pressure, k tc is the elastic coefficient of the rock mass after confining pressure, k t When no confining pressure is applied, the rock mass has an elastic coefficient, F max is the maximum force in the rock mass contact zone, F max It is equal to the jet axial reverse thrust F2, r is the abrasive particle size, and σ is the confining pressure.

[0032] Furthermore, it is characterized in that: the mining machine control instructions include mining machine body movement instructions, nozzle trajectory movement instructions, jet parameter correction instructions and safety risk warning instructions.

[0033] Further, the control instructions for the mining machine are generated based on the real-time monitoring data of the jet rock breaking dynamic monitoring module and the rock analysis and data processing module and the intelligent model of the jet cutting process parameters and the mining process parameters, including:

[0034] Based on the three-dimensional coordinates of the mining machine in the working environment fed back by the rock mass analysis and data processing module, and the three-dimensional morphology of the mining surface fed back by the rock breaking dynamic monitoring module, the optimal position and posture of the mining machine are determined by combining the jet cutting process parameters and the mining process parameter intelligent model, and the mining machine body movement instruction is generated, so that the mining machine can adjust the position and direction in real time;

[0035] Based on the jet parameters, jet axial reverse thrust, three-dimensional morphology of the mining face, and cutting and crack morphology fed back by the rock breaking dynamic monitoring module, the optimal position and posture of the jet cutting mechanism are determined in combination with the jet cutting process parameters and the mining process parameter intelligent model, and the nozzle trajectory movement instruction is generated, so that the jet cutting mechanism can adjust the movement direction and movement speed in real time;

[0036] Based on the jet parameters, jet axial reverse thrust, three-dimensional morphology of the mining face and cutting and crack morphology fed back by the rock breaking dynamic monitoring module, and the rock mass confining pressure and rock mass characteristics fed back by the rock mass analysis and data processing module, the optimal jet parameters of the jet cutting mechanism are determined in combination with the jet cutting process parameters and the mining process parameter intelligent model, and the jet parameter correction instruction is generated, so that the jet cutting mechanism can adjust the jet parameters in real time;

[0037] Based on the three-dimensional morphology of the mining face and the cutting and crack morphology fed back by the rock breaking dynamic monitoring module, as well as the rock confining pressure and rock characteristics fed back by the rock analysis and data processing module, the jet cutting process parameters and the mining process parameter intelligent model are combined to evaluate the risk factors of surrounding rock deformation, rock burst and water and mud gushing in real time, generate the safety risk warning instruction, and send a signal when the risk value is exceeded to stop the mining machine.

[0038] The beneficial effects of the present invention are:

[0039] (1) The jet cutting of the present invention uses water as a medium, which effectively suppresses dust, improves the construction environment, and is beneficial to ecological environmental protection. The jet nozzle is small in size and can realize intelligent control of hard rock cutting. The excavation section is smooth, accurately controllable, and a dynamic evaluation model for jet cutting status and effect, a dynamic evaluation model for safety risk and rock mass data, and a cutting effect evaluation model under different rock types are introduced. The jet cutting effect can be dynamically evaluated, the position and direction of the mining machine can be corrected, the nozzle movement speed and movement direction can be corrected, the mining working environment conditions can be dynamically evaluated and assessed in real time, construction process instructions and risk response measures can be issued, thereby realizing safe and efficient control of high-pressure jet mining equipment to perform adaptive cutting of ore / rock.

[0040] (2) The present invention introduces artificial intelligence and deep learning to construct intelligent models of jet cutting process parameters and mining process parameters, accurately controls the spatial posture of the high-pressure abrasive jet robot arm, realizes precise targeted cutting of the water knife, optimizes the efficiency of hydraulic crushing of hard rock and the cutting path, and realizes adaptive and precise control of key jet parameters.

[0041] (3) The present invention can realize large-scale cutting and controllable peeling of hard rock, with high hard rock crushing efficiency. Based on the characteristics of jet cutting pressure relief and energy release, it can also realize intelligent perception and active control of surrounding rock deformation. The impact load and vibration generated during the excavation process are relatively small, which can effectively reduce the damage to the surrounding rock. It can solve the problem of efficient, intelligent and green mining of complex geological conditions, hard surrounding rocks, tunnels with different cross-sections and underground projects.

[0042] (4) The high-pressure jet rock breaking and cutting of the present invention is a non-contact rock breaking without tool wear. The system adopts a jet axial reverse thrust monitoring component, a mining face laser ranging monitoring component, an acoustic emission rock fracture dynamic monitoring component, a rock mining face three-dimensional positioning component, a face equivalent confining pressure inversion component and a rock property identification component, which makes the prediction of rock stress faster, the mining efficiency of the working face higher, and the rock breaking process is less affected by complex environments such as fog, coal dust, and dirt. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0044] Figure 1 It is the technical roadmap of the principle functional module combination of the present invention;

[0045] Figure 2 Schematic diagram of the impact force of the jet on the solid plane;

[0046] Figure 3 It is the schematic diagram of the calculation of the jet axial reverse thrust;

[0047] Figure 4 Schematic diagram of the impact of the jet on the rock mass and the rock breaking depth. DETAILED DESCRIPTION

[0048] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and embodiments.

[0049] like Figure 1 As shown, a high-pressure jet intelligent mining adaptive monitoring and control system in this embodiment includes a jet rock breaking dynamic monitoring module, a rock mass analysis and data processing module, a neural network deep learning module and a mining machine controller;

[0050] The jet rock breaking dynamic monitoring module is used to: obtain jet parameters and calculate the jet axial reverse thrust, obtain the three-dimensional morphology of the mining surface, and obtain the cutting and crack morphology; wherein the three-dimensional morphology of the mining surface at least includes cutting depth information;

[0051] The rock mass analysis and data processing module is used to obtain the three-dimensional coordinates of the mining machine in the working environment, calculate the rock mass confining pressure and obtain the rock mass characteristics;

[0052] The neural network deep learning module is used to:

[0053] Collect and store historical and real-time data on jet parameters, jet axial reverse thrust, three-dimensional morphology of the mining face, and cutting and crack morphology, and establish a dynamic evaluation model for jet cutting status and effect;

[0054] Collect and store historical and real-time monitoring data of the three-dimensional coordinates of the mining machine in the working environment, rock mass confining pressure and rock mass characteristics, and establish a dynamic evaluation model for safety risks and rock mass data;

[0055] Based on the jet parameters, cutting depth information, and historical data and real-time monitoring data of rock mass characteristics, a cutting effect evaluation model for different lithologies is established;

[0056] Based on the dynamic evaluation model of jet cutting status and effect, the dynamic evaluation model of safety risk and rock mass data, and the cutting effect evaluation model, with the crushing block size, cutting depth, and surrounding rock deformation as the objective function, a jet cutting process parameter database is established, and an intelligent model of jet cutting process parameters and mining process parameters is established;

[0057] The mining machine controller is used to: determine the optimal position and posture of the mining machine and the jet cutting mechanism, the optimal jet parameters based on the real-time monitoring data of the jet rock breaking dynamic monitoring module and the rock analysis and data processing module, combined with the jet cutting process parameters and the mining process parameter intelligent model, and generate mining machine control instructions.

[0058] Jet cutting uses water as a medium, which effectively suppresses dust, improves the construction environment, and is beneficial to ecological environmental protection. The jet nozzle is small in size and can realize intelligent control of hard rock cutting. The excavation section is smooth, accurately controllable, and the introduction of dynamic evaluation models for jet cutting status and effect, dynamic evaluation models for safety risks and rock mass data, and cutting effect evaluation models under different rock types can dynamically evaluate the jet cutting effect, correct the position and direction of the mining machine, correct the nozzle movement speed and direction, dynamically evaluate the mining working environment in real time, and issue construction process instructions and risk response measures, so as to achieve safe and efficient control of high-pressure jet mining equipment for adaptive cutting of mines / rocks. In addition, by introducing artificial intelligence and deep learning to construct intelligent models of jet cutting process parameters and mining process parameters, the spatial posture of the high-pressure abrasive jet manipulator is precisely controlled to achieve precise targeted cutting of the water knife, optimize the efficiency of hydraulic crushing of hard rock and the cutting path, and achieve adaptive and accurate control of key jet parameters.

[0059] High-pressure jet cutting can achieve large-scale cutting and controllable peeling of hard rock, with high efficiency in hard rock crushing. Based on the characteristics of pressure relief and energy release of jet cutting, it can also realize intelligent perception and active control of surrounding rock deformation. The impact load and vibration generated during excavation and mining are small, which can effectively reduce surrounding rock damage and solve the problem of efficient, intelligent and green mining of tunnels and underground projects with complex geological conditions, hard surrounding rocks, and different sections. High-pressure jet rock breaking and cutting is non-contact rock breaking without tool wear. The system adopts jet axial reverse thrust monitoring components, mining face laser ranging monitoring components, acoustic emission rock fracture dynamic monitoring components, rock mining face three-dimensional positioning components, face equivalent confining pressure inversion components and rock characteristics identification components, making the prediction of rock stress faster, the mining efficiency of the working face higher, and less subject to the influence of complex environments such as fog, coal dust, and dirt during the rock breaking process.

[0060] In this embodiment, the jet rock breaking dynamic monitoring module includes a jet axial reverse thrust monitoring component, a mining face laser ranging monitoring component, and an acoustic emission rock fracture dynamic monitoring component;

[0061] The jet axial reverse thrust monitoring component is used to obtain jet parameters and calculate the jet axial reverse thrust; the mining face laser ranging monitoring component is used to obtain the three-dimensional morphology of the mining face; the acoustic emission rock fracture dynamic monitoring component is used to obtain the cutting and crack morphology.

[0062] In this embodiment, the jet parameters include: jet mass, jet velocity, jet momentum, jet area, incoming flow mass, incoming flow velocity, incoming flow momentum, the angle formed by the jet and the impact object, jet density and jet unit flow rate;

[0063] When there is no impact on the object and during the high-pressure jet cutting and breaking process, there will be an axial reaction force due to the impact of the high-pressure jet, acting on the high-pressure jet equipment. The jet axial reverse thrust monitoring component can monitor this reaction force, such as Figure 2 As shown, the jet axial reverse thrust monitoring component calculates the jet axial reverse thrust F2 as follows:

[0064] Momentum equation in the X direction: -F1 = ρQ1V1cos90° + ρQ2V2cos90° - ρQV0sinθ = -ρQV0sinθ;

[0065] Momentum equation in the Y direction: ρQ1V1-ρQ2V2-ρQV0cosθ=0;

[0066] From the Bernoulli and fluid continuity equations we get:

[0067] V0=V1+V2,

[0068] Q=Q1+Q2,

[0069]

[0070] like Figure 3 As shown, when there is no target body in the jet, the impact thrust F0: = F0 = m0V0-mV+(P0-P)A0;

[0071] When the jet hits the target, the impact thrust F1: F1 = ρQV0sin 2 θ;

[0072] When θ=90°:F1=ρQV0;

[0073] When θ=180°:F1=2ρQV0;

[0074] Then the jet axial reverse thrust F2 is: F2 = F0 + F1;

[0075] Among them: m0 represents the jet mass, V0 represents the jet velocity, P0 represents the jet momentum, A0 represents the jet area, m represents the incoming flow mass, V represents the incoming flow velocity, P represents the incoming flow momentum, θ represents the angle formed by the jet and the impacting object, ρ represents the jet density, and Q represents the unit flow rate of the jet.

[0076] In this embodiment, the mining face laser ranging monitoring component includes a three-dimensional scanning device and a laser ranging device. The three-dimensional scanning device is used to scan the three-dimensional shape of the face. The laser ranging device is used to detect the surface shape of the mining working face, the distance between the jet nozzle and the target body, the cutting depth information and the cutting seam width information, so as to obtain the three-dimensional shape of the mining face. The three-dimensional scanning device and the laser ranging device work together and are less affected by complex environments such as fog, dust, and dirt.

[0077] In this embodiment, the acoustic emission rock fracture dynamic monitoring component determines the cutting depth information of the rock by monitoring the change of the acoustic signal, and reflects the whole process of rock jet cutting cracks in real time to obtain the cutting and crack morphology. The cutting and crack morphology can be used to qualitatively evaluate the rock damage stress.

[0078] In this embodiment, the rock mass analysis and data processing module includes a rock mass mining face three-dimensional positioning component, a tunnel face equivalent confining pressure inversion component, and a rock mass characteristic identification component;

[0079] The mining face three-dimensional positioning component is used to obtain the three-dimensional coordinates of the mining machine in the working environment; the tunnel face equivalent confining pressure inversion component is used to calculate the rock mass confining pressure; and the rock mass property identification component is used to obtain rock mass properties.

[0080] The 3D positioning component of the mining face can accurately locate the cutting and mining area, clarify the direction of the cutting working face, and ensure the forward direction during the mining process, without being affected by obstacles such as dust and fog. The rock mass property recognition component can identify rock mass properties and store them in the neural network deep learning module.

[0081] In this embodiment, the process of calculating the rock mass confining pressure by the equivalent confining pressure inversion component of the tunnel face includes:

[0082] A mathematical model of rock mass confining pressure, cutting depth and jet parameters is established through the neural network deep learning module. The jet axial reverse thrust obtained by the jet axial reverse thrust monitoring component, the cutting depth information obtained by the mining face laser ranging monitoring component and the stress change obtained by the microseismic signal processing results are combined with the jet parameters to inversely learn and correct the rock mass confining pressure of the face. Based on the inversely calculated rock mass confining pressure of the face, the crack initiation stress and damage stress are qualitatively evaluated, and a map of the relationship between punching depth and confining pressure is established based on indoor simulation experiments and field tests to generate the equivalent stress diagram of the face.

[0083] In this embodiment, Figure 4 As shown, the angle of the fluid on the jet center axis when impacting the rock mass is always perpendicular to the tangent of the bottom surface of the impact crater. The dotted line in the figure is the jet center axis. Therefore, the rock breaking depth is determined by the abrasive and fluid on the center axis. After the abrasive on the jet center axis impacts the rock mass, it causes certain damage to the rock. The accumulation of this damage forms an impact crater of a certain depth. The mathematical model of rock mass confining pressure, cutting depth and jet parameters is established through the neural network deep learning module. The jet axial reverse thrust obtained by the jet axial reverse thrust monitoring component, the cutting depth information obtained by the mining face laser ranging monitoring component and the stress change obtained by the microseismic signal processing results are combined with the jet parameters to inversely learn and correct the face rock mass confining pressure. The following mathematical model is used for inversion learning and correction:

[0084]

[0085] Where: H is the cutting depth under pressure, H0 is the cutting depth without confining pressure, k tc is the elastic coefficient of the rock mass after confining pressure, k t When no confining pressure is applied, the rock mass has an elastic coefficient, F max is the maximum force in the rock mass contact zone, F max It is equal to the jet axial reverse thrust F2, r is the abrasive particle size, and σ is the confining pressure.

[0086] In this embodiment, the neural network deep learning module can be used for the construction of a neural network for jet cutting, the collection of jet cutting effects and cutting parameters, and the learning and training of the constructed neural network. The dynamic response feedback signal of the jet impacting the rock can be collected, and the mapping relationship between the jet reflection wave spectrum analysis data and the impact rock cutting characteristics can be established based on the spectrum analysis theory, and an accurate perception mechanism for the characteristics of ultra-high pressure abrasive jet cutting hard rock under rock stress field conditions can be constructed. Based on the jet cutting crushing-surrounding rock deformation adaptive program, the optimal jet parameters for collaborative cutting-surrounding rock deformation in multiple target areas are analyzed to meet the needs of multi-task adaptive collaborative operations in tunnel mining. The neural network deep learning module can continuously collect construction parameter data and construction surface image data, and collect them in a deep learning database. Through repeated iterative learning-training-verification, the accuracy of intelligent decision-making and information feedback can be continuously improved to ensure cutting speed and mining efficiency.

[0087] In this embodiment, it is characterized in that: the mining machine control instructions include mining machine body movement instructions, nozzle trajectory movement instructions, jet parameter correction instructions and safety risk warning instructions.

[0088] In this embodiment, the real-time monitoring data of the jet rock breaking dynamic monitoring module and the rock analysis and data processing module and the intelligent model of the jet cutting process parameters and the mining process parameters are used to generate the mining machine control instructions, including:

[0089] Based on the three-dimensional coordinates of the mining machine in the working environment fed back by the rock mass analysis and data processing module, and the three-dimensional morphology of the mining face fed back by the rock breaking dynamic monitoring module, the three-dimensional morphology and coordinates of the mining space and the surface morphology of the mining working face are obtained, and the optimal position and posture of the mining machine are determined by combining the jet cutting process parameters and the mining process parameter intelligent model, and the mining machine body movement instruction (i.e., correction instructions such as determining whether the mining machine moves forward, the amount of forward movement and the direction) is generated, so that the mining machine can adjust its position and direction in real time;

[0090] Based on the jet parameters, jet axial reverse thrust, mining face three-dimensional morphology, cutting and crack morphology fed back by the rock breaking dynamic monitoring module, the optimal position and posture of the jet cutting mechanism are determined in combination with the jet cutting process parameters and the mining process parameter intelligent model, and the nozzle trajectory movement instruction is generated, so that the jet cutting mechanism can adjust the moving direction and moving speed in real time. Through the jet parameters, jet axial reverse thrust, mining face three-dimensional morphology, cutting and crack morphology, it can be obtained whether the cutting depth of the jet has reached the expected target, whether the cutting depth is still increasing, whether the cutting penetrates the rock, etc. The cutting effect can be determined by relying on the jet cutting state and effect dynamic evaluation model, and the moving direction and moving speed of the jet cutting mechanism can be adjusted in real time to achieve the optimal cutting effect.

[0091] Based on the jet parameters, jet axial reverse thrust, three-dimensional morphology of the mining face, cutting and crack morphology fed back by the rock breaking dynamic monitoring module, and the rock mass confining pressure and rock mass characteristics fed back by the rock mass analysis and data processing module, the optimal jet parameters of the jet cutting mechanism are determined in combination with the jet cutting process parameters and the mining process parameter intelligent model, and the jet parameter correction instructions are generated to enable the jet cutting mechanism to adjust the jet parameters in real time. The equivalent confining pressure distribution cloud map is inverted by the change of the cutting depth of the mining face, and the mining working environment is evaluated in real time according to the safety risk and rock mass data dynamic evaluation model, so that the jet parameters of the jet cutting mechanism are adjusted to the optimal jet parameters in real time.

[0092] Based on the three-dimensional morphology of the mining face and the cutting and crack morphology fed back by the rock breaking dynamic monitoring module, as well as the rock confining pressure and rock characteristics fed back by the rock analysis and data processing module, the jet cutting process parameters and the mining process parameter intelligent model are combined to evaluate the risk factors of surrounding rock deformation, rock burst and water and mud gushing in real time, generate the safety risk warning instruction, and send a signal when the risk value is exceeded to stop the mining machine.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A high-pressure jet intelligent mining adaptive monitoring and control system, characterized by: It includes jet rock breaking dynamic monitoring module, rock mass analysis and data processing module, neural network deep learning module and mining machine controller; The jet rock breaking dynamic monitoring module is used to: obtain jet parameters and calculate the jet axial reverse thrust, obtain the three-dimensional morphology of the mining surface, and obtain the cutting and crack morphology; wherein the three-dimensional morphology of the mining surface at least includes cutting depth information; The rock mass analysis and data processing module is used to obtain the three-dimensional coordinates of the mining machine in the working environment, calculate the rock mass confining pressure and obtain the rock mass characteristics; The neural network deep learning module is used to: Collect and store historical and real-time data on jet parameters, jet axial reverse thrust, three-dimensional morphology of the mining face, and cutting and crack morphology, and establish a dynamic evaluation model for jet cutting status and effect; Collect and store historical and real-time monitoring data of the three-dimensional coordinates of the mining machine in the working environment, rock mass confining pressure and rock mass characteristics, and establish a dynamic evaluation model for safety risks and rock mass data; Based on the jet parameters, cutting depth information, and historical data and real-time monitoring data of rock mass characteristics, a cutting effect evaluation model for different lithologies is established; Based on the dynamic evaluation model of jet cutting status and effect, the dynamic evaluation model of safety risk and rock mass data, and the cutting effect evaluation model, with the crushing block size, cutting depth, and surrounding rock deformation as the objective function, a jet cutting process parameter database is established, and an intelligent model of jet cutting process parameters and mining process parameters is established; The mining machine controller is used to: determine the optimal position and posture of the mining machine and the jet cutting mechanism, the optimal jet parameters based on the real-time monitoring data of the jet rock breaking dynamic monitoring module and the rock analysis and data processing module, combined with the jet cutting process parameters and the mining process parameter intelligent model, and generate mining machine control instructions.

2. The high-pressure jet intelligent mining adaptive monitoring and control system according to claim 1 is characterized by: The jet rock breaking dynamic monitoring module includes a jet axial reverse thrust monitoring component, a mining face laser ranging monitoring component and an acoustic emission rock fracture dynamic monitoring component; The jet axial reverse thrust monitoring component is used to obtain jet parameters and calculate the jet axial reverse thrust; the mining face laser ranging monitoring component is used to obtain the three-dimensional morphology of the mining face; the acoustic emission rock fracture dynamic monitoring component is used to obtain the cutting and crack morphology.

3. The high-pressure jet intelligent mining adaptive monitoring and control system according to claim 2 is characterized by: The jet parameters include: jet mass, jet velocity, jet momentum, jet area, incoming flow mass, incoming flow velocity, incoming flow momentum, the angle formed by the jet and the impacting object, jet density and jet unit flow rate; The jet axial reverse thrust monitoring component calculates the jet axial reverse thrust F2 in the following manner: When there is no target body in the jet, the impact thrust F0: = F0 = m0V0-mV+(P0-P)A0; When the jet hits the target, the impact thrust F1: F1 = ρQV0sin 2 θ; When θ=90°:F1=ρQV0; When θ=180°:F1=2ρQV0; Then the jet axial reverse thrust F2 is: F2 = F0 + F1; Among them: m0 represents the jet mass, V0 represents the jet velocity, P0 represents the jet momentum, A0 represents the jet area, m represents the incoming flow mass, V represents the incoming flow velocity, P represents the incoming flow momentum, θ represents the angle formed by the jet and the impacting object, ρ represents the jet density, and Q represents the unit flow rate of the jet.

4. The high-pressure jet intelligent mining adaptive monitoring and control system according to claim 2 is characterized by: The mining face laser ranging monitoring component includes a three-dimensional scanning device and a laser ranging device. The three-dimensional scanning device is used to scan the three-dimensional morphology of the face. The laser ranging device is used to detect the surface morphology of the mining working face, the distance between the jet nozzle and the target body, the cutting depth information and the cutting seam width information, so as to obtain the three-dimensional morphology of the mining face.

5. The high-pressure jet intelligent mining adaptive monitoring and control system according to claim 2 is characterized by: The acoustic emission rock fracture dynamic monitoring component determines the cutting depth information of the rock by monitoring the change of the acoustic signal, and reflects the whole process of rock jet cutting cracks in real time, so as to obtain the cutting and crack morphology.

6. The high-pressure jet intelligent mining adaptive monitoring and control system according to claim 2 is characterized by: The rock mass analysis and data processing module includes a rock mass excavation face three-dimensional positioning component, a tunnel face equivalent confining pressure inversion component, and a rock mass characteristic identification component; The mining face three-dimensional positioning component is used to obtain the three-dimensional coordinates of the mining machine in the working environment; the tunnel face equivalent confining pressure inversion component is used to calculate the rock mass confining pressure; and the rock mass property identification component is used to obtain rock mass properties.

7. The high-pressure jet intelligent mining adaptive monitoring and control system according to claim 6 is characterized by: The process of calculating the rock mass confining pressure by the equivalent confining pressure inversion component of the tunnel face includes: A mathematical model of rock mass confining pressure, cutting depth and jet parameters is established through the neural network deep learning module. The jet axial reverse thrust obtained by the jet axial reverse thrust monitoring component, the cutting depth information obtained by the mining face laser ranging monitoring component and the stress change obtained by the microseismic signal processing results are combined with the jet parameters to inversely learn and correct the rock mass confining pressure of the face. Based on the inversely calculated rock mass confining pressure of the face, the crack initiation stress and damage stress are qualitatively evaluated, and a map of the relationship between punching depth and confining pressure is established based on indoor simulation experiments and field tests to generate the equivalent stress diagram of the face.

8. The high-pressure jet intelligent mining adaptive monitoring and control system according to claim 7 is characterized by: The mathematical model of rock mass confining pressure, cutting depth and jet parameters is established through the neural network deep learning module. The jet axial reverse thrust obtained by the jet axial reverse thrust monitoring component, the cutting depth information obtained by the mining face laser ranging monitoring component and the stress change obtained by the microseismic signal processing result are combined with the jet parameters to inversely learn and correct the rock mass confining pressure of the face. The inverse learning and correction are performed through the following mathematical model: Where: H is the cutting depth under pressure, H0 is the cutting depth without confining pressure, k tc is the elastic coefficient of the rock mass after confining pressure, k t When no confining pressure is applied, the rock mass has an elastic coefficient, F max is the maximum force in the rock mass contact zone, F max It is equal to the jet axial reverse thrust F2, r is the abrasive particle size, and σ is the confining pressure.

9. The high-pressure jet intelligent mining adaptive monitoring and control system according to claim 6 is characterized by: The mining machine control instructions include mining machine body movement instructions, nozzle trajectory movement instructions, jet parameter correction instructions and safety risk warning instructions.

10. The high-pressure jet intelligent mining adaptive monitoring and control system according to claim 9 is characterized in that: The generating of mining machine control instructions based on the real-time monitoring data of the jet rock breaking dynamic monitoring module and the rock mass analysis and data processing module and the intelligent model of the jet cutting process parameters and the mining process parameters includes: Based on the three-dimensional coordinates of the mining machine in the working environment fed back by the rock mass analysis and data processing module, and the three-dimensional morphology of the mining surface fed back by the rock breaking dynamic monitoring module, the optimal position and posture of the mining machine are determined by combining the jet cutting process parameters and the mining process parameter intelligent model, and the mining machine body movement instruction is generated, so that the mining machine can adjust the position and direction in real time; Based on the jet parameters, jet axial reverse thrust, three-dimensional morphology of the mining face, and cutting and crack morphology fed back by the rock breaking dynamic monitoring module, the optimal position and posture of the jet cutting mechanism are determined in combination with the jet cutting process parameters and the mining process parameter intelligent model, and the nozzle trajectory movement instruction is generated, so that the jet cutting mechanism can adjust the movement direction and movement speed in real time; Based on the jet parameters, jet axial reverse thrust, three-dimensional morphology of the mining face and cutting and crack morphology fed back by the rock breaking dynamic monitoring module, and the rock mass confining pressure and rock mass characteristics fed back by the rock mass analysis and data processing module, the optimal jet parameters of the jet cutting mechanism are determined in combination with the jet cutting process parameters and the mining process parameter intelligent model, and the jet parameter correction instruction is generated, so that the jet cutting mechanism can adjust the jet parameters in real time; Based on the three-dimensional morphology of the mining face and the cutting and crack morphology fed back by the rock breaking dynamic monitoring module, as well as the rock confining pressure and rock characteristics fed back by the rock analysis and data processing module, the jet cutting process parameters and the mining process parameter intelligent model are combined to evaluate the risk factors of surrounding rock deformation, rock burst and water and mud gushing in real time, generate the safety risk warning instruction, and send a signal when the risk value is exceeded to stop the mining machine.

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