Method and apparatus for enabling automatic operation of a material mining facility at a working face at a material mining site

Abstract

The present invention relates to a method and an apparatus for enabling a material mining facility to operate automatically at a working face (130) at a material mining site, wherein the material mining facility comprises at least one mobile and movable excavating machine (100) for producing loose material and at least one first conveying machine (105) for transporting the excavated loose material away, and wherein it is particularly configured to perform a model-based real-time simulation of the operation of the material mining facility and the corresponding excavation process of the material at the working face (130) at the material mining site based on sensor data (203) obtained by a sensor system (200, 300-315), and wherein the material mining facility is automatically operated based on the result of the real-time simulation.

Description

Technical Field

[0001] The invention relates to a method for operating a material extraction facility, in particular at a working face at a material extraction site. A computer program, a machine-readable data carrier for storing the computer program and a device capable of carrying out the method according to the invention are also objects of the invention. Background Art

[0002] Visual display systems installed in the cab and assisting the operator in working according to the planned operation are now present in mobile working or construction machines such as excavators, for example, they display the current position and orientation of the machine. In addition, in particular in the case of excavators, the position of the working arm, i.e. the coordinates of the respective tool center point (TCP) of the working arm excavator and the position of the tool are displayed. Most of these display systems can also display the global position of the machine by working in conjunction with a satellite navigation system.

[0003] A system and method for acquiring geological data and position data based on the data acquired in this way to control an excavator for earthmoving works is disclosed in DE 696 25 058 T2. Here, a geological mapping system and a geographic positioning system are used to conduct an initial survey of a specific excavation site or section. The acquired data is used by a controller to optimize the performance of the excavator. The mapping system includes a ground penetrating radar to enable a three-dimensional image of the underground geology. The geological mapping data acquired in this way are associated with historical excavator performance data in order to examine the structure of the underground geology. The positioning system that processes the GPS data includes a motorized transponder mounted on the excavator and a plurality of ground-based transponders in order to obtain an accurate geographic image of the excavation site. The steerability and excavation performance of the excavator are thereby improved, with minimal intervention by the excavator operator. Excavators with a system for acquiring the geometry of the material to be picked up and / or, in the case of storage, for acquiring the geometry of the material to be deposited, and corresponding automatic controllers are also known from US Pat. No. 6,108,949 A, DE 297 15 552 U1 and US Pat. No. 6,363,632 B1.

[0004] A method for controlling a working machine that can be used for mining, construction, agriculture or transportation is also found in DE 11 2006 000 821 T5. Here, the working machine has a motor control system, by which the motor control functions based on a mathematical model are performed. The motor control system includes a processor, a memory module, a database and an I / O or input / output interface and a network interface.

[0005] Furthermore, DE 10 2014 221 803 A1 shows a method for determining a current driving situation of a vehicle, wherein surrounding data based on a predetermined vehicle surroundings are acquired, features are extracted from the surrounding data by pattern recognition, a classification of the current driving situation is performed based on the features extracted by pattern recognition, and the result of the classification is made available. The method performs situation classification based on an existing surrounding model. The classification is performed by scene classification of images, wherein a "deep learning" method is applied. Here, an occupancy image of the surroundings is determined based on sensor data.

[0006] A method for situation recognition or classification of a monitored scene using image technology is also found in DE 10 2009 024 066 A1. Situation recognition is used for traffic monitoring or for monitoring production processes in manufacturing facilities, wherein automatic recognition of complex dynamic situations is also achieved. Here, image pixels of an image sequence of images are combined into image segments and an optical flow is calculated for a plurality of image segments. The image sequence is classified into situation classes based on the time curve of the calculated optical flow. The classification is carried out, for example, on the basis of a "hidden Markov" model, in which the transition probabilities from one state to the next in the state sequence are taken into account for situation recognition. Summary of the invention

[0007] It can be seen that the basic idea of ​​the invention is to enable the most highly automated operation of a material extraction facility consisting of at least one excavating excavator or covering excavator and at least one conveying machine or at least one conveying device, or to enable corresponding autonomous operation of the working, excavating and conveying machines present in such a facility or of the auxiliary devices used there. Accordingly, corresponding automatic excavation, covering and track planning should also be possible.

[0008] Here, it should be noted that the transfer machine can also be a transfer machine integrated at or into the earth-covering excavator. Thus, the mining excavator can be equipped with an integrated transfer bridge. A mobile crushing facility can also be provided as a transfer machine, which crushes the material supplied by the mining excavator and then transfers it to the transfer bridge or conveyor belt.

[0009] The method proposed according to the invention is based on a model-based real-time simulation of the material extraction process involved here of the material extraction facility involved here, preferably at the extraction / fracture edge or working face of the material extraction site. The model-based simulation is performed here with reference to sensor data, which are also acquired in real time by a sensor system at the facility section relevant for the material extraction process. In particular in the area of ​​the working face, the material extraction facility is operated as highly automated as possible based on the simulation results presented in real time.

[0010] It should be emphasized that the data determined by the sensors and the data derived on the basis of the model are processed together in real time in order to control the mining device at the working face.

[0011] In order to enable automated operation of at least one mining device of a material mining facility, it is necessary for the sensor system to replace the sensing and actuating capabilities or activities performed by one or more operators according to the prior art, wherein the data acquired by the sensors must be processed so that corresponding control signals can be derived for the operation of the corresponding mining device and thus enable safe and reliable operation at the working face.

[0012] Here, according to one aspect, provision can also be made that the intervention in the operation of the facility automatically derived in the above-described manner can also be carried out while taking into account the production-oriented optimization goal.

[0013] According to another aspect, at least one or a combination of the following quantities is acquired or monitored by the sensor system:

[0014] - Get the current mining geometry of the working face (actual values) in order to compare it with the setpoint values ​​of the real-time simulation;

[0015] - Acquire and monitor slope characteristics at the working face;

[0016] - Acquire and identify the damaged parts in the material to be removed at the working face, such as rocks, water pipes, etc.;

[0017] - Capture and identify intermediate or different materials, e.g. to distinguish between valuable minerals and pure overburden.

[0018] According to another aspect, the following items are processed in real time (automatically) by means of the data acquired in this way, so as to automatically perform adjustments on at least one or a combination of the following operating parameters:

[0019] - positioning the respective mining device relative to the working face and relative to the conveying machine working together with the mining device;

[0020] - triggering of process sequences and specification of process speeds, for example with reference to a pivoting operation, a driving operation, a cutting operation, a conveying operation, a lifting operation and / or a lowering operation of the respective extraction device;

[0021] - specifying the cutting depth of the cutting tool of the corresponding mining device in the material of the working face;

[0022] - Planning of trajectories of the respective mining devices for mining operations (eg travel movements of the mining devices relative to the working face).

[0023] According to another aspect, the aforementioned possible interventions in the operating parameters or the corresponding mining process can be carried out deterministically, for example on the basis of control or regulation, or (stochastically) self-learning, for example by means of an artificial neural network (ANN). The ANN can thus be trained, for example based on the device or machine behavior, for the mining edge (slope) currently being processed by the mining device.

[0024] The method proposed according to the invention thus enables real-time and continuous optimization of processes associated with mining activities at the face involved here in block operation. Based on data obtained in real time by sensors, the simulation model is enhanced here so that the method is close to optimal for the environment existing in reality.

[0025] It should be noted that the term "model-based" refers in particular to the fact that the control algorithms for the respective extraction devices are continuously optimized based on the aforementioned virtual model of the extraction process. The more data the sensors acquire in this way, the more detailed the calculations of the virtual model become, and the closer the resulting real extraction process is to its optimum.

[0026] Furthermore, the optimization task is implemented in a model-based manner based on internal process know-how and virtual possible predictions of various extraction scenarios. Thus, after the rock is assumed to be acquired by the sensor system, the extraction process can be adjusted in real time so that extraction is omitted in the area of ​​the rock.

[0027] Furthermore, the optimization criteria can also be specified (from a higher level), for example, according to the lowest possible energy requirement or with regard to maximum overburden removal or output. Alternatively or additionally, the optimization criteria can be specified according to minimum wear of a facility section or device, or can also be specified as, for example, a customer-specific profile, which has a weighted combination of the above-mentioned individual optimization objectives.

[0028] According to another aspect, in the proposed method, obstacle identification can also be performed based on available sensor data. Here, obstacles can be device-inherent (i.e. due to possible collisions of device components), facility-inherent (i.e. due to possible collisions between individual devices or with respect to the surroundings). Obstacles can be acquired in a model-based manner, i.e. derived indirectly, or can be performed directly by sensors and in this case specified by appropriate post-processing of the acquired sensor data.

[0029] In contrast, in the manual operation of work machines and corresponding overburden excavation devices in open-pit mining known from the prior art, estimation and monitoring are handed over to the corresponding device operator in order to take these aspects into account in the context of collision prevention or avoidance when controlling the device.

[0030] Therefore, according to another aspect, when a threat of collision, such as a mining device or an excavation tool, is identified, a deviation maneuver for avoiding a collision of the corresponding working machine or the working instrument of the working machine can be automatically performed, such as unconditionally stopping the movement of the device, or appropriately changing the direction of movement, or appropriately changing the operating parameters of the working machine or other machines or devices involved in the scene.

[0031] Furthermore, it should be noted that the present invention can also be applied to or used in material extraction facilities in which materials can be transported in two transport directions. Thus, a "unloading device" for placing or discharging loose materials at the working face can be provided instead of an excavating machine.

[0032] According to another aspect of the proposed method, it can be provided that classification into condition categories is performed based on the acquired sensor data. Based on the classification that has occurred, currently acquired sensor data can be classified by the corresponding classified condition category, and calculations or model-based simulations can be performed based on the classification results.

[0033] It should be noted here that the above-mentioned method step of "classification" with the help of situation categories can in the present context include the field of "machine learning" through artificial intelligence (for example through artificial neural networks), as well as generally designed decision-making and optimization algorithms, whereby decisions related to the further operation of the device or machinery involved here are made based on the geometric data of the working face acquired by sensors and the facility sections or devices involved in the mining process, the material properties of the material present at the working face to be removed, the ground data in the area of ​​the working face (for example, the water content), and, if relevant, other geological data and / or surrounding environment data in the area of ​​the working face.

[0034] Furthermore, it should be noted that existing material extraction facilities which are already automated can also be subsequently retrofitted by the method proposed according to the invention, wherein, for example, additionally the above-described model-based process simulation can be performed.

[0035] The device further proposed according to the invention is configured to automatically control the spatial movement and / or spatial alignment of a material extraction installation involved here, in particular an extraction device or extraction tool operating at a working face during an excavation or overburden removal process, by means of the proposed method.

[0036] According to one aspect of the proposed device, at least the working machines and other devices operating at the working surface are equipped with sensors for acquiring the position and the surrounding environment. The sensor system can be implemented directly by radio antennas, based on lasers ("lidar"), directly by optical cameras and / or IR cameras, or by electromagnetic sensors (transponders).

[0037] It is also conceivable to use further sensor systems based on alternative physical effects, provided that such use can additionally improve the enhancement of the surroundings and situation model with metadata. It should be emphasized that in principle any sensor system known from the prior art can be used as a sensor system. Alternatively or additionally, decentralized sensor systems are also conceivable, for example map data acquired by drone-based overflights of the material extraction facility involved here.

[0038] The relative positions of the machines or mining devices involved can also be determined in a model-based manner or by model-based simulation based on device-specific position and operating data (GPS, angle encoders, external optical recognition by image recognition).

[0039] It should be noted that full automation of the operation of a material extraction facility at a working face requires acquisition of the surroundings or environment which is as complete as possible and in particular based on real time.

[0040] According to another aspect of the proposed device, a sensor system for generating the above-mentioned sensor data, a data processing unit for carrying out the above-mentioned classification or category assignment for generating operating data are provided, wherein at least one extraction device is appropriately driven by a controller or control unit via the operating data generated in this way.

[0041] The invention can be applied in particular to material extraction facilities which can be used for ore extraction, lignite or hard coal extraction, or stone extraction or extraction of bulk materials for cement production in open-pit or underground mining.

[0042] The computer program according to the invention is configured to perform each step of the method, in particular when it is executed on a computer device or a controller. It enables the method according to the invention to be implemented on an electronic controller without the need to change its construction. For this purpose, a machine-readable data carrier is provided on which a computer program according to the invention is stored. By loading the computer program according to the invention into an apparatus or a corresponding electronic controller, an apparatus according to the invention is obtained and automatically configured to operate the material extraction facility involved here at the working face by the method according to the invention.

[0043] Other advantages and embodiments of the present invention are apparent from the description and the accompanying drawings. Elements or features that correspond or have the same functional effect are given the same reference numerals in the drawings.

[0044] It is self-evident that the features mentioned above and those still to be explained below can be used not only in the respectively given combination but also in other combinations or alone, without exceeding the scope of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic plan view of a typical spatial arrangement of a covering excavator or bucket wheel excavator of the material mining facility involved here is shown, using the example of a working face or mining edge present in an open pit mine and corresponding conveying machinery and exemplary obstacles arranged in the working area of ​​the bucket wheel excavator.

[0046] Figure 2 Exemplary embodiments of a method or a device according to the invention are shown on the basis of a combined flow chart / block diagram.

[0047] Figure 3 An exemplary embodiment of implementing a model-based situation classification for fully automated operation of a material mining facility at a working face as described herein is shown according to the present invention.

[0048] Figure 4 An exemplary embodiment of the present invention for classifying operating conditions of a material extraction facility (in this case a mining facility) detected by sensors based on a state diagram is shown.

[0049] Figure 5 Show Figure 1 An exemplary embodiment of a model-based simulation of possible movement profiles of a bucket wheel excavator is shown, based on which the movements of the excavator or its mining tool at a working face can be predicted.

[0050] Figure 6 An example mining edge or face using an open pit mining deposit is shown to further illustrate the method and apparatus according to the present invention. DETAILED DESCRIPTION

[0051] The method and the corresponding control device according to the invention for operating the material mining facility involved here at the mining edge or working face 130 of the material mining site will be described below in the exemplary embodiment of ore mining at the mining facility using a bucket wheel excavator. However, the method and the device can also be appropriately used for overburden removal facilities used elsewhere, for example for mining stone / natural stone, for mining lignite or for mining raw materials for manufacturing cement.

[0052] The installation involved here has an excavation machine, in this first embodiment a bucket wheel excavator, in the region of the working face 130. On the side substantially facing away from the working face 130, additional motorized conveying machines are arranged for transporting away the material excavated at the working face, such as one or more conveyor bridges or wheel loaders, and possibly other motorized devices, such as loading trucks or belt hooks for removing the overburden from the conveyor belt. A motorized crushing facility, not shown here, for crushing loose overburden can also be arranged in the immediate vicinity of the bucket wheel excavator.

[0053] In this exemplary embodiment, Figure 1 The device arrangement shown in the region of the working face 130 of the ore deposit comprises the above-described bucket wheel excavator 100, a conveyor bridge ("belt truck") 105 with a receiving boom 110 and a discharge boom 115, and a bucket car 120 arranged on running rails in the present exemplary embodiment. Furthermore, an obstacle 125, which in the present example consists of a tree, is schematically shown in the working area of ​​the bucket wheel excavator 110 close to the working face 130. It should be noted here that mobile devices, vehicles or persons in the region of the working face 130 can be considered as obstacles, in particular, at least also temporarily.

[0054] In a manner known per se, the bucket wheel excavator 100 comprises a bucket wheel 135 which can be rotated in a horizontal ground plane (= plane of the drawing) and usually also perpendicular to the horizontal ground plane. By means of a rotational movement of the bucket wheel 135, in particular in a ground plane corresponding to a first arrow direction 140, and a continuous advancement of the bucket wheel 135 or, corresponding to the earth-covering excavator 100, in a second arrow direction 145 at the working face 130, loose material, in this case ore-bearing rock or minerals, can be extracted.

[0055] The material or loose material excavated by the bucket wheel excavator 100 is transferred to the conveyor bridge 105 at a first transfer point 165 by means of a bucket wheel boom 150, which carries a bucket wheel 135 via a first conveyor belt 155 and a second conveyor belt 160, which operates together with the first conveyor belt 155 and is arranged at the discharge boom of the bucket wheel conveyor 100. During the excavation or covering operation of the bucket wheel excavator 100, i.e. in particular during the advancement of the bucket wheel excavator 100 in the second arrow direction 145 while the bucket wheel 135 is being moved in rotation, the first transfer point 165 must continuously remain spatially coordinated with the loading boom 110 of the conveyor bridge 105, so that no loose material falls from the second conveyor belt 160 and / or the loading boom 110 during the transfer. Therefore, there is a non-negligible risk of the bucket wheel excavator 100 and the conveyor bridge 105 or its boom 110 colliding with surrounding machines, devices, persons or obstacles 125 shown by way of example.

[0056] It should be noted that, according to an optional device arrangement, the bucket wheel excavator 100 can also directly supply the bulk material truck 120 with bulk material via a discharge boom arranged at the bucket wheel excavator 100 , in which case it is not necessary to arrange a transfer bridge 105 therebetween.

[0057] Figure 1The most fully automated operation possible of the material extraction facility shown, in particular in the area of ​​the working face 130, takes place on the basis of the method and device according to the invention by means of suitable position-resolving and condition-recording sensor systems and data processing for model-based real-time simulation of the material extraction process involved here at the working face 130 of the ore deposit.

[0058] In this first exemplary embodiment, it is assumed that Figure 3 The GPS sensors 300-315 shown in the figure are sensor systems. Alternatively or additionally, electromagnetic or optical acquisition sensors may also be used, such as laser-supported "lidar" systems, radar sensors (which are particularly known as "ground penetrating" radar systems), transponders, optical (visual) and / or IR cameras, or microwave sensors.

[0059] According to a second exemplary embodiment, the method described herein for enabling a material extraction facility to operate in a controlled manner is further improved by an adaptive method, for example by an artificial neural network (ANN). Figure 1 The method or device shown operates automatically at or in the area of ​​the working surface 130, and has operational and working safety and technical reliability. In addition to the above-described model-based simulation or as an alternative to a purely deterministic model calculation, a cognitive adaptive, i.e. stochastic or probabilistic ANN can be used here.

[0060] According to the second exemplary embodiment, the following data acquired by the aforementioned sensor systems 300-315 are considered as input quantities to the ANN, which data relate in particular to geometric and physical properties of the working face (or mining edge) 130 (e.g. to the ground or material structure) and to the surrounding properties of the bucket wheel excavator 100 at the working face 130. In addition, in this example, the operating quantities of the material extraction facility acquired by the sensors are also considered:

[0061] - the spatial geometry, topography or contour of the mining edge 130 , for example acquired (laser) optically or using radar technology;

[0062] - (internal) material properties of the mining edge 130, such as water content and possible separation layers, as detected by, for example, microwave sensors;

[0063] - possible obstacles or disruptive objects that interfere with the extraction process, such as rocks, buildings / structures or vegetation, which are detected, for example, optically (laser) or using radar technology;

[0064] - the current position of the bucket wheel excavator 100 as determined, for example, by a GPS sensor;

[0065] - the current rotation angle of the dump boom 160 of the bucket wheel excavator 100, determined, for example, by at least two GPS sensors;

[0066] - the current position of the transmission bridge 105 is again determined, for example, by means of a GPS sensor;

[0067] The current rotation angle of the transport bridge 105 is again determined, for example, by at least two GPS sensors.

[0068] A particular situation classification or category assignment of the current operating situation is performed based on the data acquired in this way.It should be noted here that the last-mentioned four data items are preferably acquired and evaluated both as relative and absolute position and / or angle data.

[0069] When classifying the current excavation or overburden removal situation at the working face, in some cases the equipment state of the bucket wheel excavator and of the conveying and auxiliary devices arranged in the area of ​​the excavator or the working face or in the surrounding area is also classified based on the acquired data or the corresponding operating quantities. Possible collisions of facility sections with one another or with objects that do not belong to the facility can be taken into account here.

[0070] When classifying the operating conditions of the facility in the area of ​​the working face 130, the quantities acquired by the sensors, i.e., for example, the precise geometric contours of the mining edge of the working face, the water content of the material there or of a possible separation layer (determining the solidity of the material to be mined), and objects at or in the area of ​​the working face (e.g., rocks, buildings or vegetation), as well as, where appropriate, the position and / or angle data of the facility sections or devices involved at the working face, are analyzed and classified or grouped into categories. On the basis of the classification results, an appropriate operating or mining plan is drawn up, wherein, when relevant, the identified, possibly imminent, critical collision operating conditions are also taken into account. For example, the classification can be carried out based on clustering methods known per se, for example, based on the known "k-means" method. The categories formed in this way then describe the specific operating conditions of the facility.

[0071] When classifying the operating conditions based on the surroundings or situation data acquired by sensors, local mapping can be additionally performed, for example by optical triangulation. Based on the results of this surroundings acquisition, a static mining plan is prepared that does not (any longer) have any system collisions inherent in the process. Due to the dynamic nature of such a plan, under the conditions of continuously changing boundary conditions in the operation of the facility, it is necessary to continuously monitor possible collisions and, if necessary, to implement further measures to avoid collisions.

[0072] After the operating conditions are classified, they are transformed into specific facility and / or process states in the area of ​​the mining edge. This state transition is Figure 4. In addition to the current operating status, the facility status here also takes into account the previous facility status. This allows the operating status to be evaluated over a longer time period. The state transition also allows synchronization between the various facility sections. In the present exemplary embodiment, the technical implementation of the state transition is carried out in a manner known per se by means of an appropriate state machine.

[0073] Figure 4 The state diagram shown starts from an initial state 400 ('S0') of a facility or mining process. In an intermediate state 405 ('S1') caused by a first state transition 420 ('Z1'), two possible subsequent states 410, 415 ('S2' and 'S3') occur in this example. Based on the classification of the previous operating conditions, the first subsequent state 410 ('S2') is generated based on the second state transition 425 ('Z2'), or the second subsequent state 415 ('S3') is generated based on the third state transition 430 ('Z3').

[0074] Now assume that one of the two possible subsequent states 410 ('S2'), 415 ('S3') contains a collision situation with the aforementioned obstacle or destructive body, such as the subsequent state 415 ('S3'). Such a collision situation can be, for example, in the further operation of the facility. Figure 1 An imminent possible impact between the discharge boom 160 of the bucket wheel excavator 100 and the loading boom 110 of the transfer bridge 105 is shown.

[0075] After the current operating state of the facility and the possible subsequent states of the facility with a possible collision have been determined in the described manner, in the present exemplary embodiment, a control or regulation strategy suitable for the current facility state is first selected for effectively preventing such a collision. This selection can be made, for example, by selecting set point values ​​for the corresponding regulation of the individual devices, or can be made together for all devices in the group of devices of the overburden removal facility.

[0076] Figure 2 Exemplary embodiments of a method or device according to the invention are shown on the basis of a combined block diagram / flow chart.

[0077] In the present exemplary embodiment, for the sake of simplicity, the method shown is based on a sensor system 200 which is used only to acquire the geometrical profile of the mining edge 130 and obstacles 125 which may be located at the working face.

[0078] In the present exemplary embodiment, the sensor system 200 comprises a (laser optical) lidar system 310 (see Figure 1 and Figure 3 ).

[0079] Predictive planning of the mining process at the face 130 is carried out by means of an industrial PC (IPC) 205, which is in particular capable of operating in real time and is known per se, using the sensor data 203 supplied by the lidar system 200. Based on the sensor data 203, appropriate operating or control data 207 for the excavator 100 and the necessary control data for the machines or devices involved in the mining process at least in the immediate surroundings of the excavator 100 are generated or calculated in real time for the automation of the mining process at the face 130.

[0080] Based on the control data 207 provided by the IPC 205, all device groups 100, 105 located in the area of ​​the working surface 130 are driven by a programmable logic controller (PLC) 210. By means of this drive, possible collisions with the above-mentioned obstacles 125 can also be effectively avoided by appropriate evasive maneuvers and / or by shutting down the relevant actuators of one of the devices 100, 105 involved. Here, the drive is realized, for example, based on suitable drive commands or corresponding pivoting movements of the bucket wheel 135 of the excavator 100 and the movable conveyor bridge 110 of the conveyor bridge 105.

[0081] Alternatively or additionally, the method for automated operation of the facility involved here can be based on a device arranged in Figure 1 130 in the area of ​​the working surface 130 shown in FIG. The above-mentioned model calculation can include, on the one hand, a model-based simulation of possible movement profiles (kinematics) and thus a corresponding prediction of the movement of the bucket wheel 135 of the excavator 100 and the local transfer machine group 105.

[0082] Figure 5 An exemplary embodiment of a model-based simulation is schematically shown in FIG. 1 . This exemplary scenario concerns a possible collision between a bucket wheel excavator 500 and an obstacle 530, which is detected by sensors in the manner described in the area of ​​a working face or excavation edge 502. In simple terms, the excavator 500 according to the model comprises only a chain drive 505 arranged at a boom 510 and a bucket wheel 515. Since the present model calculation preferably concerns the removal of material at the excavation edge 502, the transport equipment necessary for transporting the excavated material away is not taken into account in this simplification. The bucket wheel 515 is again designed in a simplified form to be pivotable horizontally only in the direction of the arrow 520 shown, in order to be able to excavate or remove material at the excavation edge 502 by means of this pivoting operation.

[0083] According to the available operating plan or the planned equipment utilization for the mining operation of the shown facility, the excavator 500 is fed in the shown arrow direction 525. In the shown right-hand end position 522 of the bucket wheel 515, a further forward movement of the excavator in the arrow direction 525 will lead to a collision detected by the sensor between the bucket wheel 515 and the obstacle 530. In order to effectively avoid the collision of the bucket wheel 515 with the obstacle 530, three optional operating modes or forward modes or avoidance paths of the excavator 500 are generated in this example.

[0084] According to the first possible avoidance path 535, the excavator 500 performs a movement extending to the left, whereby the bucket wheel 515 is brought over the obstacle 530 on the left in its right-hand end position 522 and is therefore no longer able to collide with the obstacle 530. The advantage of this avoidance path 535 is that no downtime is required for the operation of the facility or the excavator 500. However, it has the disadvantage that a significant spatial and temporal manipulation results in a planned excavation process, wherein the excavation geometry at the excavation edge 502 is significantly changed and the excavation process must therefore be appropriately adjusted.

[0085] According to the second possible avoidance path 540, the excavator 500 performs a turning movement to the right, whereby the bucket wheel 515, in particular in its left-hand end position 524, is brought past the obstacle 530 on the right and thus no longer collides with the obstacle 530. However, it must be noted here that bringing the excavator 500 in front of the obstacle 530 leads to at least partial shutdown of the facility, since in the best case the excavator 500 reaches the excavation edge 502 with a delay and can only resume excavation operation thereafter.

[0086] According to the third operating mode 545, the excavator 500 performs an emergency stop just before reaching the obstacle 530, i.e. causes the aforementioned shutdown of the facility. In this way, the aforementioned collision with the obstacle 530 can also be effectively prevented. Compared with this third alternative, the first two alternatives are preferred because this allows or ensures the continuous operation of the excavator or the facility, because the aforementioned shutdown time of the entire facility will be avoided if possible.

[0087] By 计划 or the corresponding envelope and the area A covered by the obstacle 530 障碍物 By comparing, the possible collision probability can be determined. 障碍物 Yes A 计划 If the condition "part or subset of" is met, a collision is predicted here. If the condition is not met, no collision is predicted.

[0088] Referring to the exemplary condition classification, Figure 3 The reference is shown in more detail in Figure 2Describes the forecast calculation method.

[0089] exist Figure 1 The conveyor chain and the conveyor bridge 105 formed at the working face 130 of the bucket wheel excavator 100 are shown in simplified form. Figure 2 The sensor system described, in this exemplary embodiment, comprises a lidar measurement system 310 and three GPS position sensors 300, 305, 315. The lidar measurement system 310 is arranged at the bucket wheel 135 of the excavator 100, while the three GPS sensors 300, 305, 315 are arranged at the excavator 100 and at the conveyor belts of the unloading boom 160 of the excavator 100 and the loading boom 110 of the conveyor bridge 105.

[0090] It should be noted that the current angle data of the illustrated set of devices can be calculated by standard trigonometric methods from the position or position data supplied by the GPS sensors 300, 305, 315. The angle data include in particular a first angle 335 between the forward direction 145 of the bucket wheel excavator 100 and the alignment of the conveyor belt of the unloading boom 160, as well as a second angle 340 between the forward direction 145 and the bucket wheel boom 150, and a third angle 345 between the forward direction 350 of the conveyor bridge 105 and the alignment of its loading boom 110 or unloading boom 115.

[0091] Furthermore, it should be noted that the above-described position sensors 300 , 305 , 315 can also be implemented by a camera system, a radar system or by a laser-based measuring system (eg lidar) and a corresponding evaluation of the image data acquired in this way.

[0092] The prediction or planning of the excavation process at the working face 130 is here carried out by appropriately adapting the forward speed of the bucket wheel excavator 100 in the direction of movement 145 of the excavator 100 in the direction of the working face 130. Furthermore, the process planning is carried out in such a way that collisions with the aforementioned obstacles are effectively prevented.

[0093] The classification method can be performed in a known manner by decision trees or by pattern recognition (e.g. by neural networks) in an analytical, numerical manner. In this example, the classification method divides the operating conditions into categories (class assignment) by a so-called "classifier" or a corresponding algorithm in a known manner.

[0094] In contrast, in the category assignment method, the operating status is assigned to an already existing category.

[0095] In the classification method, the above-mentioned sensor data obtained at the working face and, where relevant, the current position and / or angle data of the extraction and conveying machines 100, 105 and, if relevant, the loose material trucks 120 are acquired and the data obtained in this way are classified into the previously empirically determined condition classes in the above-mentioned manner. Based on the classification results, the material extraction facility can then be operated fully automatically at least in the area of ​​the working face 130.

[0096] exist Figure 6 At the working face 600 shown in the schematic side view of , a bucket wheel excavator 635 (shown here only schematically) including the above-mentioned bucket wheel 640 will move from left to right (in the plane of the paper) and mine material in the area of ​​the shown mining edge 605. In this scenario, there are already two mining layers or planes 610, 610' and 615, 615', and the excavator (not shown) is currently performing mining operations on the upper layer 615, 615'.

[0097] In this exemplary embodiment, a radar-based sensor system 650 is arranged at the excavator 635, through which the geometry of the slope 620, in particular the geometry of the mining edge 605, is acquired on the one hand, and on the other hand, using the ground-penetrating radar technology known per se, possible destructive bodies, rocks or separation layers inside the working face or the mining edge can be acquired.

[0098] It should be noted that the arrangement of the sensor system at the excavator (acting, so to speak, as a sensor carrier) is merely exemplary and that the sensor system may optionally also be arranged in a mobile form at a wing device such as a ground-based drone or an aerial drone.

[0099] The technical processing tasks of the sensor system 650 involve acquiring the following data as accurately as possible and with the lowest possible time offset:

[0100] - Acquiring the actual state of the geometry of the mining edge 605 as an actual value in order to be able to compare it with the corresponding setpoint value calculated by the model-based simulation with the lowest possible time offset;

[0101] - Acquisition of slope characteristics in the region of the working face 600 , in particular at the mining edge 605 ;

[0102] - Acquire any disruptive bodies, such as rocks, present at or within the mining edge 605;

[0103] - Obtaining the (internal) structure of the material at the mining edge 605, for example in order to distinguish the quality of material present in the ground and for distinguishing between valuable minerals that can be used to obtain ore and usable cover, for determining the precise location of any underground or mining operations that may have been carried out previously, the precise location of separation layers of material located in the ground caused by this or other reasons, and for geological analysis in the area of ​​the slope 620.

[0104] The driving of the excavator, in particular the advancement of the excavator in the aforementioned direction of travel and the pivoting movement of the bucket wheel, is carried out on the basis of a controller based on minimization of the deviation between the aforementioned actual value and the set point value, or alternatively or additionally, can be carried out on the basis of a self-learning AI process, which is based on the device behavior related to the current slope to be processed.

[0105] The geometric model of the mining edge 605 is used as the basis for the model-based simulation and the actual state acquired by the sensor with geological data is additionally enhanced. Thus, by means of the radar sensor 645, in addition to the above-mentioned geometric acquisition of the slope feature 620 or the mining edge 605, the spatial position of the fracture segment or structure 625 enclosed inside the mining edge 605 is acquired by the sensor, which is presented as an example in the present scenario. The fracture segment 625 may originate from an earlier underground or mine operation and therefore serve only as (pure) unusable cover. On the other hand, the fracture segment 625 may also correspond to the above-mentioned valuable minerals to be discovered by the sensor in order to be carefully mined accordingly. In the present scenario, the model also includes rocks 630 in the upper mining layer 610, which have not yet been exposed or have been somewhat exposed.

[0106] A detailed excavation plan for operating the excavator 635 is obtained by simulation. Here, based on the above comparison between the data acquired by the sensors and the corresponding data calculated based on the model, the following operating parameters of the excavator 635 are automatically set or readjusted:

[0107] - Positioning of the bucket wheel excavator relative to the mining block;

[0108] - the travel (forward) speed of the excavator and the pivoting speed of the bucket wheel;

[0109] - Divide the slices in the corresponding excavation blocks (corresponding to the slope height);

[0110] - cutting depth of bucket wheel;

[0111] -Trajectory planning for excavators used for mining.

Claims

1. A method for enabling a material extraction facility to automatically operate at a working face (130) at a material extraction site, wherein: The material mining facility comprises at least one mobile and movable mining machine (100) for producing loose material and at least one conveying machine (105) for transporting the mined loose material away, wherein, based on sensor data (203) obtained by a sensor system (200, 300-315), a model-based real-time simulation is performed on the operation of the material mining facility at the working face (130) of the material mining site and the corresponding mining process of the material, and the material mining facility is automatically operated based on the results of the real-time simulation, It is characterized in that generating first control data (207) for the at least one mining machine (100) in real time based on the acquired sensor data (203), the first control data being suitable for automating the mining process at the working face (130), and Second control data is generated for facility sections or devices involved in the mining process located in the surroundings of the working face (130) or the surroundings of the at least one mining machine (100).

2. The method according to claim 1, characterized in that Process planning is performed on the mining process at the working face (130) based on the acquired sensor data (203).

3. The method according to claim 1 or 2, characterized in that: Referring to the acquired sensor data (203), current operating conditions of the material mining facility in the area of ​​the working face (130) are classified into condition categories, and based on the classification results, possible upcoming operating conditions requiring intervention at the material mining facility are identified.

4. The method according to claim 3, characterized in that Based on the acquired sensor data (203), when classifying the operating conditions of the material extraction facility in the area of ​​the working face (130), one or more of the following group of technical quantities is determined: - The geometrical profile of the mining edge (605) of the working face; - the strength of the material to be removed; - possible separation layers in the material to be removed; - an object arranged in the region of the excavation edge (605); - Position and / or angle data of the facility sections or devices involved at the working surface.

5. The method according to claim 4, characterized in that When classifying the operating conditions of the material extraction facility in the area of ​​the working face (130), one or more of the following two groups of technical variables acquired by sensors are additionally taken into account: - slope characteristics at the working surface; - Different materials at the working surface.

6. The method according to claim 4, characterized in that The sensor data (203) acquired by the sensors and classified when necessary are processed in real time to automatically perform settings for at least one or more of the following group of operating parameters of the material extraction facility: - positioning of the at least one mining machine (100) relative to the working surface; - positioning of the at least one mining machine (100) with reference to at least one conveying machine operating in technical cooperation with the mining machine (100); - Triggering of process sequences; - specification of process speed; - specification of a cutting depth of a cutting tool of said at least one mining machine (100); - Travel movement of the at least one mining machine (100) relative to the working surface.

7. The method according to claim 6, characterized in that The sensor data (203) acquired by sensors and classified when necessary is processed in real time by a self-learning artificial neural network, wherein the mining process at the mining edge (605) is trained based on a predetermined machine behavior or device behavior.

8. The method according to claim 1 or 2, characterized in that: Control data (207) are generated, by which the at least one mining machine (100) and the at least one first conveying machine (105) are driven in a collision-avoiding (210) manner, wherein appropriate offset maneuvers are performed and / or at least one actuator of at least one of the mining machines / conveying machines involved is shut down and / or collision-avoiding drive commands and / or pivoting and lifting movements of the at least one mining machine (100) and the at least one first conveying machine (105) are executed.

9. The method according to claim 4, characterized in that The strength of the material to be removed is based on its water content.

10. A computer program configured to perform each step of the method according to any one of claims 1 to 9.

11. An apparatus configured to control a mining / conveying facility that can be used for open-pit mining, the apparatus comprising at least one mining machine (100) for producing loose material and at least one conveying machine (105) for transporting the mined loose material away by a method according to any one of claims 1 to 9.

12. The device according to claim 11, characterized in that It comprises a sensor system (200, 300-315) for generating sensor data (203), a data processing unit (205) for performing a classification according to any one of claims 5 to 7 and for generating control data (207), by means of which the at least one mining machine (100) and the at least one conveying machine (105) are driven by a control unit (210).

13. The device according to claim 11 or 12, characterized in that The sensor system (200, 300-315) comprises one or more of the following group of sensors: - Radar antennas and / or ground penetrating radar systems; -Optical or laser-based sensors; - Optical camera or IR camera; -Electromagnetic sensors; -GPS sensor; - Angular encoder.

14. The device according to claim 13, characterized in that The optical or laser-based sensor is a lidar and the electromagnetic sensor is a transponder.

Images