Method, device and equipment for controlling transportation equipment in strip mine

Abstract

The embodiment of the invention discloses a transportation equipment control method, device and equipment in a strip mine, belongs to the technical field of equipment control, is applied to an edge computing node deployed in an operation area of the strip mine, and comprises the following steps: obtaining a digital twin model constructed based on three-dimensional data of the transportation equipment and the strip mine; acquiring first state data of the transportation equipment; on the basis of the first state data, the operation state of the transportation equipment is predicted in a digital twin model, and a prediction result is obtained; and generating control information for the transportation equipment based on the prediction result. According to the cooperative control architecture of the distributed edge computing and digital twinborn model in the strip mine, the transportation equipment is controlled through the edge computing nodes, the real-time performance of communication is improved, the delay of instruction transmission is reduced, the transportation equipment is controlled in time before an exception occurs, and the safety of the transportation equipment is improved. And the overall working safety of the strip mine is improved.

Description

Technical Field

[0001] This application relates to the field of equipment control technology, and in particular to a method, device and equipment for controlling transportation equipment in open-pit mines. Background Technology

[0002] Continuous transportation equipment in open-pit mines is an important part of modern mine production, mainly including equipment such as electric shovels, dump trucks, crushing stations, and belt conveyors.

[0003] In related technologies, a control system for transportation equipment is constructed using GPS positioning, wireless communication, and a central control platform, with data transmission achieved through 4G, 5G, or WiFi (wireless) networks. Specifically, the terminals mounted on the transportation equipment periodically report equipment location and status information to the central control platform, which then issues unified control commands.

[0004] However, in the aforementioned technologies, the central control platform uniformly controls the transportation equipment. Since the central control platform is located in the rear command center of the open-pit mine, the real-time communication is low, which leads to high delays in command transmission. Summary of the Invention

[0005] This application provides a method, apparatus, and equipment for controlling transportation equipment in open-pit mines, which can improve the real-time performance of communication and reduce the delay in command transmission. The technical solution is as follows: On one hand, embodiments of this application provide a method for controlling and detecting transportation equipment in an open-pit mine, applied to at least one edge computing node deployed in the operating area of ​​the open-pit mine, the method comprising: A digital twin model was constructed based on the three-dimensional data of the transportation equipment and the open-pit mine. Acquire the initial status data of the transport equipment; Based on the first state data, the operating state of the transportation equipment is predicted in the digital twin model to obtain the prediction result; Control information for the transport equipment is generated based on the prediction results.

[0006] On the other hand, embodiments of this application provide a transportation equipment control device in an open-pit mine, applied to edge computing nodes deployed in the working area of ​​the open-pit mine, the device comprising: The model building module is used to construct a digital twin model based on the three-dimensional data of the transportation equipment and the open-pit mine; The data acquisition module is used to acquire the first state data of the transportation equipment; The equipment prediction module is used to predict the operating status of the transportation equipment in the digital twin model based on the first state data, and obtain the prediction result. A control generation module is used to generate control information for the transport equipment based on the prediction results.

[0007] In another aspect, embodiments of this application provide an edge computing node, which includes a processor and a memory. The memory stores a computer program, which is loaded and executed by the processor to implement the above-described method for controlling transportation equipment in open-pit mines.

[0008] In another aspect, embodiments of this application provide a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for controlling transportation equipment in an open-pit mine.

[0009] In another aspect, embodiments of this application provide a computer program product that, when the computer program product is run, causes an edge computing node to execute the aforementioned method for controlling transportation equipment in an open-pit mine.

[0010] Compared with the prior art, the technical solution provided in this application can bring the following beneficial effects: (1) By deploying edge computing nodes in the work area and combining them with digital twin models, the operating status of the transportation equipment is predicted based on the first state data of the transportation equipment. Then, control information is generated based on the prediction results. A collaborative control architecture of "distributed edge computing and digital twin model" in open-pit mine is proposed. Controlling the transportation equipment through edge computing nodes is beneficial to improving the real-time performance of communication and thus reducing the delay of command transmission. (2) Predicting the operating status of transportation equipment through digital twin models is beneficial to control the transportation equipment in a timely manner before anomalies occur, thereby improving the overall safety of open-pit mine operations and further ensuring the smooth operation of open-pit mine operations. Attached Figure Description

[0011] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of a transportation equipment control system in an open-pit mine according to an embodiment of this application; Figure 2 This is a flowchart of a method for controlling transportation equipment in an open-pit mine, provided in one embodiment of this application; Figure 3 This is a flowchart of a method for controlling transportation equipment in an open-pit mine, provided in another embodiment of this application; Figure 4 This is a block diagram of a transportation equipment control device in an open-pit mine according to an embodiment of this application; Figure 5This is a block diagram of a transportation equipment control device in an open-pit mine, provided in another embodiment of this application. Detailed Implementation

[0012] Please refer to Figure 1 The diagram illustrates a control system for transportation equipment in an open-pit mine, according to an embodiment of this application. This control system may include: transportation equipment 10, edge computing nodes 20, and a central server 30.

[0013] The transport equipment 10 refers to specific working equipment in an open-pit mine. Exemplarily, the transport equipment 10 includes equipment such as electric shovels, dump trucks, crushing stations, and belt conveyors. In this embodiment, the transport equipment 10 is deployed in the working area of ​​the open-pit mine, and each device included in the transport equipment 10 is equipped with a smart terminal 11. The smart terminal 11 periodically transmits the status data of the transport equipment 10 to the edge computing node 20 when the transport equipment 10 is in operation.

[0014] Edge computing nodes 20 are used to control the transport equipment 10. Exemplarily, edge computing nodes 20 can be backend servers, PCs (Personal Computers), or electronic devices such as terminals carried by workers; this embodiment does not limit this. In this embodiment, edge computing nodes 20 are deployed in the operating area of ​​the open-pit mine, and each edge computing node 20 includes a digital twin model 21 constructed based on the three-dimensional data of the transport equipment 10 and the open-pit mine. After receiving the aforementioned state data, the edge computing node 20 predicts the operating state of the transport equipment 10 in the digital twin model based on the state data, obtains the prediction result, and then generates control information for the transport equipment 10 based on the prediction result. Optionally, the control information includes a communication channel and control commands. The edge computing node 20 sends control commands to the intelligent terminal 11 of the transport equipment 10 through the communication channel. Further, the intelligent terminal 11 controls the transport equipment 10 based on the control commands. Optionally, the transport equipment control system in the open-pit mine includes multiple edge computing nodes 20. Exemplarily, such as... Figure 1 As shown, one edge computing node 20 corresponds to one working area.

[0015] The central server 30 is used for data recording and overall planning of the open-pit mine. Exemplarily, the central server 30 can be an electronic device such as a mobile phone, tablet, wearable device, backend server, server cluster, or PC (Personal Computer). In this embodiment, the central server 30 is deployed in the backend command area of ​​the open-pit mine. Optionally, in this embodiment, after generating the aforementioned control information, the edge computing node 20 synchronously sends the control information to the central server 30, which records the control information. Furthermore, if the digital twin model 21 is updated due to the aforementioned three-dimensional data transformation, the central server 30 sends the updated digital twin model's relevant data to the edge computing node 20.

[0016] Optionally, the transport equipment 10, the edge computing node 20, and the central server 30 communicate via a network.

[0017] Please refer to Figure 2 This document illustrates a flowchart of a method for controlling transport equipment in an open-pit mine, according to an embodiment of this application. The method is applied to... Figure 1 The image shows an edge computing node 20 deployed in the working area of ​​an open-pit mine within a transportation equipment control system. The method may include the following steps (201-204): Step 201: Obtain a digital twin model constructed based on the three-dimensional data of the transportation equipment and the open-pit mine.

[0018] In this embodiment of the application, when controlling the transportation equipment, the edge computing node acquires a digital twin model constructed based on the three-dimensional data of the transportation equipment and the open-pit mine. The digital twin model refers to a virtual physical model generated by mapping the transportation equipment and the open-pit mine in virtual space. Exemplarily, the three-dimensional data includes, but is not limited to, at least one of the following: the size of the transportation equipment, the type of transportation equipment, the quantity of transportation equipment, and the terrain data of the open-pit mine.

[0019] Alternatively, the digital twin model can be built locally on the edge computing node, or it can be as described above. Figure 1 The central server in the application is constructed, but this embodiment does not limit it.

[0020] In one possible implementation, the digital twin model is constructed by edge computing nodes. Optionally, the edge computing nodes acquire 3D data of transportation equipment and open-pit mines in the work area they are responsible for, and then construct a digital twin model based on the 3D data.

[0021] In another possible implementation, the digital twin model is constructed by a central server. Optionally, the central server acquires all the 3D data of the transportation equipment and open-pit mine, and then constructs an overall digital twin model based on the 3D data. Further, the overall digital twin model is divided based on the work areas managed by the edge computing nodes, and the digital twin model corresponding to the work area is sent to the edge computing nodes.

[0022] Step 202: Obtain the first state data of the transport equipment.

[0023] In this embodiment of the application, after obtaining the aforementioned digital twin model, the edge computing node acquires first state data of the transportation equipment. This first state data is used to indicate the operating status of the transportation equipment. Exemplarily, the first state data includes, but is not limited to, at least one of the following: location information, attitude information, speed information, load information, etc.

[0024] Optionally, in this embodiment, the transport equipment is equipped with a smart terminal, and the edge computing node obtains the first state data through the smart terminal on the transport equipment. For example, the transport equipment includes an RTK-GPS (Real-Time Kinematic Global Positioning System) or UWB (Ultra-Wideband) unit and an IMU (Inertial Measurement Unit) sensor. The smart terminal collects centimeter-level position information through the RTK-GPS or UWB unit and attitude information, velocity information, etc., through the IMU sensor. Based on the position information, attitude information, velocity information, etc., the smart terminal generates the first state data, and then sends the first state data to the edge computing node.

[0025] Optionally, in this embodiment, the smart terminal includes a simplified digital twin model. When data transmission between the smart terminal and the edge computing node is impossible, the smart terminal predicts the operating status of the transportation equipment based on the simplified digital twin model to generate local control commands, and then controls the transportation equipment based on these local control commands.

[0026] Step 203: Based on the first state data, predict the operating state of the transportation equipment in the digital twin model to obtain the prediction result.

[0027] In this embodiment of the application, after obtaining the first state data, the edge computing node predicts the operating state of the transportation equipment in the digital twin model based on the first state data, and obtains the prediction result.

[0028] Optionally, the edge computing nodes simulate the operating status of the transportation equipment in a digital twin model based on the first state data, thereby obtaining prediction results.

[0029] For example, the operating status includes, but is not limited to, at least one of the following: position change information, attitude change information, speed change information, load change information, etc.

[0030] Step 204: Generate control information for the transportation equipment based on the prediction results.

[0031] In this embodiment, after obtaining the prediction results, the edge computing node generates control information for the transportation equipment based on the prediction results. This control information is used to control the transportation status through the intelligent terminal mounted on the transportation equipment.

[0032] In summary, the technical solution provided in this application proposes a collaborative control architecture of "distributed edge computing and digital twin model" in open-pit mines. This architecture utilizes edge computing nodes deployed in the work area, combined with a digital twin model, to predict the operating status of the transportation equipment based on its first state data. Control information is then generated based on the prediction results. Controlling the transportation equipment through edge computing nodes improves real-time communication and reduces command transmission latency. Furthermore, predicting the operating status of the transportation equipment using a digital twin model allows for timely control before anomalies occur, enhancing the overall safety of the open-pit mine and ensuring smooth operation.

[0033] In addition, the intelligent terminal of the transportation equipment includes a simplified digital twin model. When the intelligent terminal and the edge computing node cannot transmit data, the intelligent terminal predicts the operating status of the transportation equipment based on the simplified digital twin model to generate local control commands. In other words, even in the event of data transmission failure due to network failures, the transportation status can still be controlled based on the local simplified digital twin model, further improving the overall safety of the open-pit mine.

[0034] The following section provides a detailed introduction to the methods for generating control information.

[0035] In an exemplary embodiment, step 304 above includes the following steps: 1. Generate control commands for transportation equipment based on the prediction results.

[0036] In this embodiment of the application, after obtaining the above-mentioned prediction results, the edge computing node generates control commands for the transportation equipment based on the prediction results. These control commands are used to control the transportation status.

[0037] Optionally, the control instructions include a first control instruction.

[0038] Optionally, in this embodiment, if the edge computing node predicts an abnormal event in the transportation equipment based on the prediction results, it generates a first control command for the transportation equipment. This first control command is used to prevent the abnormal event from occurring.

[0039] For example, abnormal events include, but are not limited to, at least one of the following: cycle time mismatch event, insufficient safety distance event, etc. For example, the transport equipment includes more than one related device. A cycle time mismatch event refers to an event where at least two devices in the transport equipment have significantly different operating states, resulting in an inability to work collaboratively. For example, if the prediction result indicates that a first related device in the transport equipment has a cycle time mismatch event, the first control command generated by the edge computing node is a cycle time control command, which is used to control the first related device to adjust its operating cycle time to prevent the occurrence of the cycle time mismatch event; if the prediction result indicates that a second related device in the transport equipment has an insufficient safety distance event, the first control command generated by the edge computing node is a speed control command, which is used to control the second related device to adjust its speed to prevent the occurrence of the insufficient safety distance event.

[0040] Optionally, the control instructions may also include a second control instruction.

[0041] Optionally, if the edge computing node predicts that there are no abnormal events in the transportation equipment based on the prediction results, it generates a second control command for the transportation equipment. This second control command is used to control the transportation equipment to maintain its current operating state.

[0042] Optionally, to enhance the security of control commands during transmission, after generating the control commands, the edge computing node encrypts and signs the commands using the national standard SM9 algorithm (Identity-Based Cryptography Algorithm), and records the hash value of the control commands in a blockchain distributed ledger to achieve tamper-proof evidence preservation. Optionally, this blockchain is stored on the edge computing node and / or the central server.

[0043] 2. Based on the instruction type of the control instruction, allocate communication channels to the control instruction.

[0044] In this embodiment, after obtaining the control command, the edge computing node allocates a communication channel to the control command based on the command type. The control information includes the control command and the communication channel.

[0045] Optionally, in this embodiment, when the control command is a conventional collaborative command, the edge computing node allocates a standard communication channel to the control command; when the control command is an emergency braking command, the edge computing node allocates a dedicated communication channel to the control command. The dedicated communication channel exclusively occupies high-frequency communication resources to ensure that the transmission delay of critical commands (i.e., emergency braking commands) does not exceed 50ms. Furthermore, actual testing shows that in the complex electromagnetic environment of open-pit mines, the above communication channel allocation design increases the control command transmission success rate from 85% in traditional schemes to 99.5%.

[0046] Optionally, after obtaining the aforementioned control information, the edge computing node sends a control command to the intelligent terminal mounted on the transportation equipment based on the communication channel in the control information. Further, the intelligent terminal controls the transportation equipment based on the control command.

[0047] It should be noted that the above local control commands do not require channel allocation.

[0048] In summary, the technical solution provided in this application generates control commands for transportation equipment based on prediction results. Furthermore, it allocates corresponding communication channels to control commands based on their command types, and performs reasonable channel resource allocation for control commands. This helps reduce communication congestion caused by too many control commands occupying the same communication channel, improves command transmission efficiency, and thus reduces communication latency.

[0049] In addition, when an abnormal event is predicted based on the prediction results, a first control command is generated for the transported equipment. This first control command is used to prevent the occurrence of abnormal events, changing the control method for the transport equipment from "passive intervention" in related technologies to "prevention in advance," thereby improving the operational safety of various equipment in open-pit mines.

[0050] In addition, control commands are allocated, and a dedicated communication channel is assigned to emergency braking commands. This dedicated communication channel exclusively occupies high-frequency communication resources, which improves the transmission efficiency and success rate of critical commands (i.e., emergency braking commands).

[0051] Please refer to Figure 3 This illustrates a flowchart of a method for controlling transport equipment in an open-pit mine, provided in another embodiment of this application. The method is applied to... Figure 1 The image shows an edge computing node 20 deployed in the working area of ​​an open-pit mine within a transportation equipment control system. The method may include the following steps (301-307): Step 301: Obtain a digital twin model constructed based on the three-dimensional data of the transportation equipment and the open-pit mine.

[0052] Step 302: Obtain the first state data of the transport equipment.

[0053] Step 303: Based on the first state data, predict the operating state of the transportation equipment in the digital twin model to obtain the prediction result.

[0054] Step 304: Generate control information for the transportation equipment based on the prediction results.

[0055] Steps 301-304 above and Figure 2 Steps 201-204 in the embodiment are similar; see details below. Figure 2 Examples are not detailed here.

[0056] Step 305: Based on the control information, predict the operating status of the transportation equipment in the digital twin model to obtain the control results.

[0057] In this embodiment of the application, after obtaining the above-mentioned control information, the edge computing node predicts the operating status of the transportation equipment in the digital twin model based on the control information to obtain the control result.

[0058] Optionally, the edge computing nodes perform multi-timescale simulations of the operating status of the transportation equipment in the digital twin model based on the control commands in the control information, thereby obtaining the control results.

[0059] Step 306: Obtain the second state data fed back by the transport equipment based on the control information.

[0060] In this embodiment, after acquiring the aforementioned control information, the edge computing node acquires second state data fed back by the transportation equipment based on the control information. This second state data indicates the operating state of the transportation equipment after control is applied based on the control information. Exemplarily, the second state data includes, but is not limited to, at least one of the following: position information, attitude information, speed information, load information, etc.

[0061] Optionally, after controlling the transportation equipment based on control commands, the aforementioned smart terminal further acquires second state data and feeds the second state data back to the edge computing node.

[0062] Step 307: If the second state data matches the control result, determine that the control information is valid.

[0063] In this embodiment, after acquiring the second state data and the control result, if the second state data matches the control result, the edge computing node determines that the control information is valid, meaning the control information has successfully taken effect on the transport equipment. Here, the matching of the second state data and the control result means that the difference between each piece of information contained in the second state data and each piece of information contained in the control result is less than a preset threshold. Exemplarily, this preset threshold can be any value, and can be flexibly set and adjusted according to actual conditions; this embodiment does not limit this.

[0064] Optionally, in this embodiment, if the second state data does not match the control result, the edge computing node switches the communication link to a backup frequency band and retransmits the control command in the control information through the backup frequency band. Then, step 307 is repeated to obtain new second state data and compare it with the control result until it is determined whether the control information is valid. Additionally, if the control information is determined to be invalid, the edge computing stage issues a prompt message to remind staff that the transport equipment is in an abnormal state.

[0065] It should be noted that the above description of the predictive function of the digital twin model from the perspective of comparing the second state information with the control result is, in practical applications, the edge computing node can continuously compare the physical state data (equivalent to the first state data and the second state data) collected by the above-mentioned smart terminal with the virtual state data (equivalent to the above-mentioned prediction result and the above-mentioned control result) calculated by the digital twin; furthermore, if the difference between the physical state data and the virtual state data is greater than or equal to the above-mentioned preset threshold, the transportation equipment is determined to be in an abnormal state and the above-mentioned prompt information is immediately issued.

[0066] In summary, the technical solution provided in this application, after obtaining control information, predicts the operating state of the transportation equipment in the digital twin model based on the control information to obtain the control result, and obtains the second state data fed back by the transportation equipment based on the control information. The control result and the second state data are compared. If the second state data matches the control result, the control information is determined to be valid (i.e., successfully effective). This reasonably utilizes the predictive function of the digital twin model and further verifies whether the result corresponding to the control information is the same as the expected result after the control information is executed, thereby improving the safety and reliability of the control of the transportation equipment.

[0067] In addition, if the second state data does not match the control result, the communication link is switched to the backup frequency band, and the control commands in the control information are retransmitted through the backup frequency band. The timely activation of the backup frequency band helps to improve the success rate of command transmission and further improves the reliability of the control of the transportation equipment.

[0068] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0069] Please refer to Figure 4 This diagram illustrates a block diagram of a transportation equipment control device in an open-pit mine according to an embodiment of this application. The device has the function of implementing the aforementioned transportation equipment control method in an open-pit mine. This function can be implemented in hardware or by hardware executing corresponding software. The device is applied to an edge computing node deployed in the operating area of ​​the open-pit mine. The device can be an edge computing node itself or be located within an edge computing node. The device may include: a model building module 410, a data acquisition module 420, an equipment prediction module 430, and a control generation module 440.

[0070] The model building module 410 is used to build a digital twin model based on the three-dimensional data of the transportation equipment and the open-pit mine.

[0071] The data acquisition module 420 is used to acquire the first state data of the transport equipment.

[0072] The equipment prediction module 430 is used to predict the operating status of the transportation equipment in the digital twin model based on the first state data, and obtain the prediction result.

[0073] The control generation module 440 is used to generate control information for the transport equipment based on the prediction results.

[0074] In an exemplary embodiment, the control generation module 440 is configured to: Based on the prediction results, control commands are generated for the transport equipment. Based on the instruction type of the control instruction, a communication channel is allocated to the control instruction; wherein, the control information includes the control instruction and the communication channel.

[0075] In an exemplary embodiment, the control generation module 440 is configured to: If an abnormal event is predicted for the transport equipment based on the prediction results, a first control command is generated for the transport equipment. The first control instruction is used to prevent the occurrence of the abnormal event, and the control instruction includes the first control instruction.

[0076] In an exemplary embodiment, the control generation module 440 is configured to: When the control command is a conventional cooperative command, a standard communication channel is allocated to the control command; When the control command is an emergency braking command, a dedicated communication channel is allocated to the control command; wherein the dedicated communication channel exclusively occupies high-frequency communication resources.

[0077] In an exemplary embodiment, such as Figure 5 As shown, the device also includes a data verification module 450.

[0078] The equipment prediction module 430 is also used to predict the operating status of the transportation equipment in the digital twin model based on the control information, and obtain the control result.

[0079] The data acquisition module 420 is also used to acquire the second status data fed back by the transport equipment based on the control information.

[0080] The data verification module 450 is used to determine that the control information is valid if the second state data matches the control result.

[0081] In an exemplary embodiment, such as Figure 5 As shown, the device further includes a frequency band switching module 460 and a command retransmission module 470.

[0082] The frequency band switching module 460 is used to switch the communication link to a backup frequency band when the second state data does not match the control result.

[0083] The instruction retransmission module 470 is used to retransmit the control instructions in the control information through the spare frequency band.

[0084] In an exemplary embodiment, the data acquisition module 420 is used to acquire the first state data through a smart terminal mounted on the transportation equipment; wherein the smart terminal includes a simplified version of the digital twin model; when the smart terminal and the edge computing node cannot transmit data, the smart terminal predicts the operating state of the transportation equipment based on the simplified version of the digital twin model to generate local control commands.

[0085] In summary, the technical solution provided in this application proposes a collaborative control architecture of "distributed edge computing and digital twin model" in open-pit mines. This architecture utilizes edge computing nodes deployed in the work area, combined with a digital twin model, to predict the operating status of the transportation equipment based on its first state data. Control information is then generated based on the prediction results. Controlling the transportation equipment through edge computing nodes improves real-time communication and reduces command transmission latency. Furthermore, predicting the operating status of the transportation equipment using a digital twin model allows for timely control before anomalies occur, enhancing the overall safety of the open-pit mine and ensuring smooth operation.

[0086] In an exemplary embodiment, an edge computing node is also provided, the edge computing node including a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement the above-described method for controlling transportation equipment in an open-pit mine.

[0087] In an exemplary embodiment, a non-transitory computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the above-described method for controlling transport equipment in an open-pit mine.

[0088] In an exemplary embodiment, a computer program product is also provided, which, when run, causes an edge computing node to execute the above-described method for controlling transportation equipment in an open-pit mine.

[0089] The above embodiments are merely illustrative of the technical concept and features of the present invention, intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and should not be construed as limiting the scope of protection of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of the present invention is defined by the appended claims rather than the foregoing description, and thus all changes falling within the meaning and scope of the equivalents of the claims are intended to be included within the invention.

[0090] It should be understood that "multiple" as used herein refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. Furthermore, the step numbers described herein are merely illustrative of one possible execution order. In some other embodiments, the steps may not be executed in numerical order, such as two steps with different numbers being executed simultaneously, or two steps with different numbers being executed in the reverse order of the illustration. This application does not limit this.

[0091] The above description is merely an exemplary embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for controlling transport equipment in open-pit mines, characterized in that, The method, applied to edge computing nodes deployed in the operational area of ​​an open-pit mine, includes: A digital twin model was constructed based on the three-dimensional data of the transportation equipment and the open-pit mine; Acquire the initial status data of the transport equipment; Based on the first state data, the operating state of the transportation equipment is predicted in the digital twin model to obtain the prediction result; Control information for the transport equipment is generated based on the prediction results.

2. The method according to claim 1, characterized in that, The generation of control information for the transport equipment based on the prediction results includes: Based on the prediction results, control commands are generated for the transport equipment. Based on the instruction type of the control instruction, a communication channel is allocated to the control instruction; wherein, the control information includes the control instruction and the communication channel.

3. The method according to claim 2, characterized in that, The step of generating control commands for the transport equipment based on the prediction results includes: If an abnormal event is predicted for the transport equipment based on the prediction results, a first control command is generated for the transport equipment. The first control instruction is used to prevent the occurrence of the abnormal event, and the control instruction includes the first control instruction.

4. The method according to claim 2, characterized in that, The allocation of a communication channel to the control command based on the command type includes: When the control command is a conventional cooperative command, a standard communication channel is allocated to the control command; When the control command is an emergency braking command, a dedicated communication channel is allocated to the control command; wherein the dedicated communication channel exclusively occupies high-frequency communication resources.

5. The method according to any one of claims 1 to 4, characterized in that, After generating control information for the transport equipment based on the prediction results, the method further includes: Based on the control information, the operating status of the transportation equipment is predicted in the digital twin model to obtain the control result; Obtain the second state data fed back by the transport equipment based on the control information; If the second state data matches the control result, the control information is determined to be valid.

6. The method according to claim 5, characterized in that, After acquiring the second state data fed back by the transport equipment based on the control information, the method further includes: If the second state data does not match the control result, the communication link will be switched to a backup frequency band; The control commands in the control information are retransmitted via the spare frequency band.

7. The method according to any one of claims 1 to 4, characterized in that, The acquisition of the first state data of the transport equipment includes: The first status data is obtained through the intelligent terminal mounted on the transport equipment; The smart terminal includes a simplified version of the digital twin model; when the smart terminal and the edge computing node cannot transmit data, the smart terminal predicts the operating status of the transportation equipment based on the simplified version of the digital twin model to generate local control commands.

8. A control device for transport equipment in an open-pit mine, characterized in that, An edge computing node deployed in the working area of ​​an open-pit mine, the device comprising: The model building module is used to construct a digital twin model based on the three-dimensional data of the transportation equipment and the open-pit mine; The data acquisition module is used to acquire the first state data of the transportation equipment; The equipment prediction module is used to predict the operating status of the transportation equipment in the digital twin model based on the first state data, and obtain the prediction result. A control generation module is used to generate control information for the transport equipment based on the prediction results.

9. An edge computing node, characterized in that, The edge computing node includes a processor and a memory, the memory storing a computer program that is loaded and executed by the processor to implement the method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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