An operating state evaluation method for an underground mine roadway tunneling device

Abstract

The present application discloses a method for evaluating the operating status of underground mine tunneling equipment, which belongs to the field of computer technology. When the tunneling equipment is in operation, the equipment operating parameters of the tunneling equipment, the geological parameters of the underground mine in which it is located, a video in the tunneling direction, and the physiological parameters and operation set of the operator in the tunneling equipment are obtained. The equipment operating parameters, geological parameters, and the video are used to determine the environmental operating status of the tunneling equipment, thereby obtaining the operating status of the tunneling equipment under the influence of the external environment. The equipment operating parameters, physiological parameters, and the operation set are used to determine the human-machine interaction status of the tunneling equipment, thereby obtaining the interaction between the operator and the tunneling equipment. In combination with the environmental operating status and the human-machine interaction status, the equipment operating status of the tunneling equipment is determined, thereby achieving an evaluation of the equipment operating status of the tunneling equipment during operation and improving the safety of operations using the tunneling equipment.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method for evaluating the operating status of underground mine tunneling equipment. Background Art

[0002] In underground mine tunneling operations, the traditional method for assessing equipment operating status is manual inspection. Relying on experience and simple inspection tools such as wrenches and calipers, operators regularly inspect tunneling equipment, checking for external damage and loose parts.

[0003] However, manual inspection is usually delayed, that is, operators can only conduct inspections after the tunneling equipment has completed its work, and it is difficult to timely evaluate the operating status of the tunneling equipment during its operation, which may pose a safety hazard. Summary of the Invention

[0004] The present application provides a method for evaluating the operating status of underground mine tunneling equipment. The method can evaluate the operating status of the tunneling equipment during operation, detect abnormalities of the tunneling equipment in a timely manner, and improve the safety of tunneling equipment operations. The technical solution is as follows:

[0005] In one aspect, a method for evaluating the operating status of underground mine tunneling equipment is provided, the method comprising:

[0006] When the tunneling equipment is in operation, obtaining equipment operating parameters of the tunneling equipment, geological parameters of the underground mine in which the tunneling equipment is located, video of the tunneling equipment in the direction of excavation, and physiological parameters and operation sets of an operator in the tunneling equipment;

[0007] determining an environmental operating state of the tunneling equipment based on the equipment operating parameters, the geological parameters, and the video, wherein the environmental operating state is used to indicate an operating condition of the tunneling equipment under an external environment;

[0008] determining a human-machine interaction state of the tunneling equipment based on the equipment operating parameters, the physiological parameters, and the operation set, wherein the human-machine interaction state is used to represent the interaction between the operator and the tunneling equipment;

[0009] The equipment operating state of the tunneling equipment is determined based on the environmental effect operating state and the human-machine effect state of the tunneling equipment.

[0010] In one aspect, a device for evaluating the operating status of underground mine tunneling equipment is provided, the device comprising:

[0011] an acquisition module for acquiring, while the tunneling equipment is in operation, equipment operating parameters of the tunneling equipment, geological parameters of the underground mine in which the tunneling equipment is located, video of the tunneling equipment in the tunneling direction, and physiological parameters and operation sets of an operator in the tunneling equipment;

[0012] an environmental effect operating state determining module, configured to determine the environmental effect operating state of the tunneling equipment based on the equipment operating parameters, the geological parameters, and the video, wherein the environmental effect operating state is used to indicate the operating condition of the tunneling equipment under the influence of the external environment;

[0013] a human-machine interaction state determination module, configured to determine the human-machine interaction state of the tunneling equipment based on the equipment operating parameters, the physiological parameters, and the operation set, wherein the human-machine interaction state is used to represent the interaction between the operator and the tunneling equipment;

[0014] The equipment operation state determination module is used to determine the equipment operation state of the tunneling equipment based on the environmental effect operation state and the human-machine effect state of the tunneling equipment.

[0015] On the one hand, a computer device is provided, which includes one or more processors and one or more memories, wherein at least one computer program is stored in the one or more memories, and the computer program is loaded and executed by the one or more processors to implement the operating status evaluation method of the underground mine tunnel excavation equipment.

[0016] On the one hand, a computer-readable storage medium is provided, in which at least one computer program is stored. The computer program is loaded and executed by a processor to implement the operating status evaluation method of the underground mine tunneling equipment.

[0017] On the one hand, a computer program product or computer program is provided, which includes a program code, which is stored in a computer-readable storage medium. A processor of a computer device reads the program code from the computer-readable storage medium, and the processor executes the program code, so that the computer device executes the above-mentioned underground mine tunnel excavation equipment operation status assessment method.

[0018] Through the technical solution provided in the embodiment of the present application, when the tunneling equipment is working, the equipment operating parameters of the tunneling equipment, the geological parameters of the underground mine in which it is located, the video in the tunneling direction, and the physiological parameters and operation set of the operator in the tunneling equipment are obtained. The equipment operating parameters, geological parameters and the video are used to determine the environmental action operating state of the tunneling equipment, thereby obtaining the operating conditions of the tunneling equipment under the influence of the external environment. The equipment operating parameters, physiological parameters and the operation set are used to determine the human-machine action state of the tunneling equipment, thereby obtaining the interaction between the operator and the tunneling equipment. In combination with the environmental action operating state and the human-machine action state, the equipment operating state of the tunneling equipment is determined, and the equipment operating state of the tunneling equipment is evaluated during the working process, abnormalities of the tunneling equipment are discovered in a timely manner, and the safety of operations using the tunneling equipment is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0020] Figure 1 Schematic diagram of an implementation environment of an operating status assessment method for underground mine tunneling equipment provided in an embodiment of the present application;

[0021] Figure 2 This is a flow chart of an operating status evaluation method for underground mine tunneling equipment provided in an embodiment of the present application;

[0022] Figure 3 This is a flow chart of another method for evaluating the operating status of underground mine tunneling equipment provided in an embodiment of the present application;

[0023] Figure 4 This is a schematic structural diagram of an operating status evaluation device for underground mine tunneling equipment provided in an embodiment of the present application;

[0024] Figure 5 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0026] In this application, the terms "first", "second", etc. are used to distinguish identical or similar items with substantially the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there any limitation on the quantity and execution order.

[0027] Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to achieve better results.

[0028] Machine learning (ML) is a multidisciplinary field that encompasses probability theory, statistics, approximation theory, convex analysis, and algorithmic complexity theory. It specifically studies how computers can simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge sub-models to continuously improve their performance. Machine learning is at the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications span all areas of AI.

[0029] Underground mines (metal and nonmetal underground mines) are mining sites and their ancillary facilities that use adits, inclined shafts, ramps, vertical shafts, etc. as entrances and exits, go deep below the surface, and mine metal minerals, radioactive minerals, chemical raw materials, building materials, etc.

[0030] Mine tunnels: Mine tunnels are various passages and spaces excavated during underground mining for the purpose of transporting ore, ventilation, drainage, pedestrians and other related operations.

[0031] Tunneling equipment refers to the various mechanical equipment and tools used in underground tunneling operations. The main functions of this equipment are to crush rock, transport ore, and support the tunnel to ensure safe and efficient tunneling.

[0032] In the prior art, maintenance personnel manually inspect the status of tunneling equipment before and after use to improve its safety. However, while the tunneling equipment is tunneling in the roadway, operators are often focused on the excavation operation and may not notice any abnormalities in the equipment's operating status, leading to safety hazards when using the equipment for excavation.

[0033] After adopting the technical solution provided in the embodiment of the present application, the operating status of the excavation equipment can be evaluated during the excavation process of the excavation equipment in the tunnel, so as to timely discover abnormalities of the excavation equipment and improve the safety of operations using the excavation equipment.

[0034] The following describes the implementation environment of the embodiment of the present application. Figure 1 The implementation environment includes a data processing device 101 , a device controller 102 , and multiple sensors 103 .

[0035] The data processing device 101 is used to obtain data collected by multiple sensors 103 through the equipment controller 102, and process the obtained data to obtain the operating status of the tunneling equipment. The data processing device 101 is usually set outside the tunnel to improve the stability of data processing.

[0036] The equipment controller 102 is used to control the tunneling equipment. It is electrically connected to multiple sensors 103 and can directly acquire data collected by these sensors 103. After acquiring the data collected by these sensors 103, the equipment controller 102 forwards the data to the data processing device 101 for processing. This is because the data processing capabilities of the equipment controller 102 are typically far weaker than those of a dedicated data processing device 101. In this embodiment of the present application, the data processing device 101 and the equipment controller 102 are connected via a wired or wireless network.

[0037] Multiple sensors 103 are used to collect relevant data during the operation of the tunneling equipment, providing data support for the technical solution provided in the embodiment of the present application.

[0038] The following describes the application scenarios of the technical solutions provided in the embodiments of the present application. The technical solutions provided in the embodiments of the present application can be applied in scenarios of excavating various types of tunnels, for example, in excavating prospecting tunnels, production tunnels, and mining tunnels. The technical solutions provided in the embodiments of the present application can also be applied to various types of excavation equipment equipped with the above-mentioned equipment controller 102 and multiple sensors 103. In other words, as long as the relevant data provided in the embodiments of the present application can be collected, the technical solutions provided in the embodiments of the present application can be applied to evaluate the operating status of the excavation equipment.

[0039] In addition, the application of the technical solution provided in the embodiments of the present application requires data processing equipment with strong computing power in order to complete data processing in a timely manner.

[0040] The following describes the operating status evaluation method for underground mine tunneling equipment provided in an embodiment of the present application. Figure 2 This is a flow chart of an operating status evaluation method for underground mine tunneling equipment provided in an embodiment of the present application. Figure 2 Taking the execution subject as a data processing device as an example, the method includes the following steps.

[0041] 201. When the tunneling equipment is working, the data processing equipment obtains the equipment operating parameters of the tunneling equipment, the geological parameters of the underground mine where the tunneling equipment is located, the video of the tunneling direction of the tunneling equipment, and the physiological parameters and operation set of the operator in the tunneling equipment.

[0042] Tunneling equipment is used for excavating tunnels in mines. It includes a tunneling assembly, which refers to a component that interacts with rock mass to break it, such as a tunneling head. The tunneling equipment also includes a personnel cabin, where the operator is located, operating the tunneling equipment from within the cabin. The operating parameters of the tunneling equipment are parameters collected during operation and can be used in conjunction with other parameters to determine the operating status of the tunneling equipment. Geological parameters include various indicators that describe the characteristics and properties of the geological body (rock mass in an underground mine). The tunneling direction is the direction of movement of the tunneling equipment during operation. In this embodiment of the present application, the video in the tunneling direction includes environmental video and tunneling assembly video. The environmental video records the environmental conditions in the tunneling direction, while the tunneling assembly video records the operating conditions of the tunneling assembly. The operator's physiological parameters represent the operator's physiological state, such as body temperature, blood pressure, and blood oxygen saturation. The operator's operation set represents the operator's operational status.

[0043] 202. The data processing device determines an environmental effect operating state of the tunneling device based on the device operating parameters, the geological parameters, and the video. The environmental effect operating state is used to represent an operating condition of the tunneling device under the influence of the external environment.

[0044] The external environment refers to the rock mass in the direction of excavation, and the external environment refers to the interaction between the rock mass and the excavation components of the excavation equipment. When excavating different types of rock mass, the effects of the interaction between the excavation components and the rock mass vary. In some embodiments, the operating state of the environmental effect is represented by environmental state description information, which describes the operating state of the environmental effect in the form of text or latent vectors.

[0045] 203. The data processing device determines a human-machine interaction state of the tunneling device based on the device operating parameters, the physiological parameters, and the operation set. The human-machine interaction state is used to represent the interaction between the operator and the tunneling device.

[0046] When an operator operates the tunneling equipment, the operator's operation will affect the operating state of the tunneling equipment. Accordingly, due to the high power and vibration intensity of the tunneling equipment, the tunneling equipment has a greater impact on the operator. In the event of an abnormality in the operating state of the tunneling equipment, the impact on the operator will also change. This change can be represented by the operator's physiological state. Therefore, by determining the operator's physiological state, the operating state of the tunneling equipment can also be inferred. In some embodiments, the human-machine interaction state is represented by human-machine state description information, which is information that describes the human-machine interaction state in the form of text or latent vectors.

[0047] 204. The data processing device determines the equipment operation state of the tunneling device based on the environmental operation state of the tunneling device and the human-machine operation state.

[0048] The device operating status represents the operating status of the tunneling equipment, and can be used to determine whether there are any abnormalities in the operation of the tunneling equipment. In some embodiments, the device operating status is represented by device operating status description information, which is information describing the device operating status in the form of text or latent vectors.

[0049] Through the technical solution provided in the embodiment of the present application, when the tunneling equipment is working, the equipment operating parameters of the tunneling equipment, the geological parameters of the underground mine in which it is located, the video in the tunneling direction, and the physiological parameters and operation set of the operator in the tunneling equipment are obtained. The equipment operating parameters, geological parameters and the video are used to determine the environmental action operating state of the tunneling equipment, thereby obtaining the operating conditions of the tunneling equipment under the influence of the external environment. The equipment operating parameters, physiological parameters and the operation set are used to determine the human-machine action state of the tunneling equipment, thereby obtaining the interaction between the operator and the tunneling equipment. In combination with the environmental action operating state and the human-machine action state, the equipment operating state of the tunneling equipment is determined, and the equipment operating state of the tunneling equipment is evaluated during the working process, abnormalities of the tunneling equipment are discovered in a timely manner, and the safety of operations using the tunneling equipment is improved.

[0050] The above steps 201-204 are a brief introduction to the operating status evaluation method of the underground mine tunnel boring equipment provided in the embodiment of the present application. The operating status evaluation method of the underground mine tunnel boring equipment provided in the embodiment of the present application will be more clearly explained below with some examples. Figure 3 Taking the execution subject as a data processing device as an example, the method includes the following steps.

[0051] 301. When the tunneling equipment is working, the data processing equipment obtains the equipment operating parameters of the tunneling equipment, the geological parameters of the underground mine where the tunneling equipment is located, the video of the tunneling direction of the tunneling equipment, and the physiological parameters and operation set of the operator in the tunneling equipment.

[0052] Tunneling equipment is used for tunneling tunnels in mines. It includes a tunneling assembly, which interacts with rock mass to break it. For example, the tunneling assembly is a tunneling head, which rotates to break the rock mass. The present embodiment does not limit the shape and size of the tunneling assembly. The tunneling equipment also includes a personnel cabin, where the operator of the tunneling equipment resides. The operator operates the tunneling equipment from within the cabin. The operating parameters of the tunneling equipment are parameters collected during operation and can be used in conjunction with other parameters to determine the operating status of the tunneling equipment. Geological parameters include various indicators that describe the characteristics and properties of the geological mass (rock mass in an underground mine). In some embodiments, geological parameters include rock hardness, rock density, and rock porosity. The tunneling direction is the direction of movement of the tunneling equipment during operation. In the present embodiment, the video in the tunneling direction includes environmental video and tunneling assembly video. The environmental video records the environmental conditions in the tunneling direction, while the tunneling assembly video records the operating conditions of the tunneling assembly. The operator's physiological parameters are used to represent the operator's physiological state. For example, physiological parameters include body temperature, blood pressure, and blood oxygen saturation. The operator's operation set is used to represent the operator's operating status. In addition, the data processing equipment is not set in the tunneling equipment. This is because the tunneling equipment has large vibrations during operation, which may affect the normal operation of the data processing equipment. The technical solution provided in the embodiment of the present application requires a large amount of computation, and the equipment controller of the tunneling equipment cannot meet the computing power requirements. Therefore, the equipment controller sends the collected data to the external data processing equipment, and the data processing equipment performs the data processing.

[0053] In one possible implementation, while the tunneling equipment is operating, the data processing device obtains, from the equipment controller of the tunneling equipment, equipment operating parameters of the tunneling equipment, video footage of the tunneling direction, and a set of physiological parameters and operations of the operator. The data processing device also obtains, from a storage medium, geological parameters of the underground mine in which the tunneling equipment is located.

[0054] The tunneling equipment is equipped with multiple sensors. Among these sensors, the equipment operation sensor is used to collect the equipment's operating parameters, and the visual sensor is used to capture video footage of the tunneling direction. All sensors are connected to the equipment controller, which transmits the collected data to the tunneling equipment. Operators operate the tunneling equipment through the equipment controller, allowing the controller to directly access the operator's operational data. Furthermore, the personnel compartment of the tunneling equipment is equipped with a physiological parameter sensor, which requires the operator to actively wear it. If the operator is not wearing the physiological parameter sensor, the tunneling equipment will not start, thus ensuring the integrity of the data collected.

[0055] In some embodiments, equipment operating parameters include a temperature parameter set, a vibration parameter set, operating parameters of the tunneling assembly, a strain parameter set, movement parameters, and cabin environmental parameters of the personnel compartment. The temperature parameter set includes the temperatures of multiple components on the tunneling equipment. For example, the temperature parameter set includes the operating temperature of the tunneling assembly and the operating temperature of the engine. The operating temperature is collected by a temperature sensor. The engine is used to drive the tunneling equipment to move and rotate. The vibration parameter set includes the vibration parameters of the tunneling assembly and the engine. The vibration parameters are used to indicate vibration conditions and are collected by a vibration sensor. Operating parameters include speed and torque, which are collected by a speed sensor and a torque sensor, respectively. The strain parameter set includes strain at multiple locations on the tunneling assembly, which indicates the degree of deformation at multiple locations on the tunneling assembly. The strain is collected by a strain sensor. Movement parameters include the speed and direction of movement of the tunneling equipment and are collected by a movement sensor. Cabin environmental parameters include cabin vibration data, cabin temperature, cabin pressure, and cabin gas content within the personnel compartment. These are collected by a vibration sensor, a temperature sensor, an air pressure sensor, and a gas content sensor.

[0056] In some embodiments, the video in the excavation direction includes an environmental video and an excavation component video. The environmental video is used to record the excavation status of the excavation equipment in the tunnel, and the excavation component video is used to record the working status of the excavation component of the excavation equipment.

[0057] In some embodiments, physiological parameters include body temperature, blood pressure, and blood oxygen saturation, which are collected through wearable physiological parameter sensors, such as smart bracelets.

[0058] 302. The data processing device determines rock mass action operation information of the tunneling device based on the device operation parameters and the geological parameters.

[0059] The rock mass action operation information is used to indicate the operation of the tunneling equipment under the influence of the rock mass. The geological parameters can indicate the geological conditions of the underground mine where the tunneling equipment is located. The equipment operation parameters and geological parameters can be used to reflect the operation of the tunneling equipment under the influence of the rock mass. In some embodiments, the rock mass action operation information is in the form of text or latent vectors.

[0060] In one possible embodiment, the equipment operating parameters include a temperature parameter set, a vibration parameter set, and operating parameters of the tunneling assembly. The data processing device determines operating temperature information of the tunneling equipment based on the temperature parameter set, the geological parameters, and the operating parameters. The temperature parameter set includes the operating temperature of the tunneling assembly and the operating temperature of the engine of the tunneling equipment. The data processing device determines operating vibration information of the tunneling equipment based on the vibration parameter set, the geological parameters, and the operating parameters. The vibration parameter set includes the vibration parameters of the tunneling assembly and the vibration parameters of the engine. Based on the operating temperature information and the operating vibration information, the data processing device determines rock mass action information of the tunneling equipment.

[0061] The operating temperature information indicates the reasonableness of the operating temperature of the tunneling assembly when interacting with a rock mass with the specified geological parameters at the specified operating parameters. Correspondingly, the operating vibration information indicates the reasonableness of the vibration of the tunneling assembly when interacting with the rock mass with the specified geological parameters at the specified operating parameters. In some embodiments, the vibration parameters include vibration frequency and vibration intensity.

[0062] In this embodiment, the operating temperature information of the tunneling assembly is determined based on the temperature parameter set, geological parameters, and the operating parameters of the tunneling assembly, thereby identifying the reasonableness of the tunneling assembly's operating temperature. The operating vibration information of the tunneling assembly is determined based on the vibration parameter set, geological parameters, and operating parameters, thereby identifying the reasonableness of the tunneling assembly's vibration conditions. The rock mass effect information is determined based on the operating temperature information and the operating vibration information, thereby reflecting the impact of the rock mass on the operation of the tunneling assembly.

[0063] In order to explain the above embodiment more clearly, the above embodiment will be described in several parts below.

[0064] In the first part, the data processing device determines the working temperature operation information of the tunneling component based on the temperature parameter set, the geological parameter and the working parameter.

[0065] In one possible implementation, the data processing device determines a reference operating temperature range for the tunneling assembly and a reference operating temperature range for the engine based on the geological parameters and the operating parameters. The data processing device determines component temperature operating information for the tunneling assembly based on the reference operating temperature range for the tunneling assembly and the operating temperature of the tunneling assembly. The data processing device determines engine temperature operating information for the engine based on the reference operating temperature range for the engine and the operating temperature of the tunneling equipment engine. The data processing device determines operating temperature information for the tunneling equipment based on the component temperature operating information and the engine temperature operating information.

[0066] Among them, the reference operating temperature range is the temperature range of the operating temperature under normal circumstances. When the engine and tunneling components remain unchanged, there is a corresponding relationship between the operating parameters, geological parameters and operating temperature of the tunneling components. The reason why the reference operating temperature range is determined instead of the reference operating temperature is because the working temperature may also be affected by air heat dissipation and water flow. The reference operating temperature range takes into account the impact of other factors on the working temperature.

[0067] For example, the data processing device inputs the geological parameter and the operating parameter into a temperature range prediction model. Using the temperature range prediction model, the data processing device extracts features from the geological parameter and the operating parameter to obtain geological features corresponding to the geological parameter and operating features corresponding to the operating parameter. The data processing device then fuses the geological features and the operating features using the temperature range prediction model to obtain an operating temperature prediction feature. Using the temperature range prediction model, the data processing device maps the operating temperature prediction feature to obtain a reference operating temperature range for the tunneling assembly and a reference operating temperature range for the engine. If the operating temperature of the tunneling assembly is within the reference operating temperature range, the data processing device determines the component temperature operating information of the tunneling assembly as first component temperature operating information, indicating that the component temperature of the tunneling assembly is normal, i.e., has a high degree of rationality. If the operating temperature of the tunneling assembly is not within the reference operating temperature range, the data processing device determines the component temperature operating information of the tunneling assembly as second component temperature operating information, indicating that the component temperature of the tunneling assembly is abnormal, i.e., has a low degree of rationality. If the engine's operating temperature is within the engine's reference operating temperature range, the data processing device determines the engine's engine temperature operating information as first engine temperature operating information. The first engine temperature operating information indicates that the engine's engine temperature is normal, i.e., is relatively reasonable. If the engine's operating temperature is not within the engine's reference operating temperature range, the data processing device determines the engine's engine temperature operating information as second engine temperature operating information. The second engine temperature operating information indicates that the engine's engine temperature is abnormal, i.e., is relatively low in reasonableness. The data processing device concatenates the component temperature operating information and the engine temperature operating information to obtain the operating temperature operating information of the tunneling equipment.

[0068] Among them, the temperature range prediction model is a regression model, which is trained based on multiple sample geological parameters, multiple sample working parameters and corresponding annotated reference temperature ranges. The embodiment of this application does not limit the structure and training method of the temperature range prediction model.

[0069] In order to more clearly illustrate the technical solution provided by the above example, the method of obtaining two reference operating temperature ranges by using the temperature range prediction model in the above example is explained below.

[0070] In some embodiments, the data processing device inputs the geological parameter and the operating parameter into a temperature range prediction model, performs multiple full joins on the geological parameter and the operating parameter through the temperature range prediction model, and obtains geological characteristics corresponding to the geological parameter and operating characteristics corresponding to the operating parameter. The data processing device fuses the geological characteristics and the operating characteristics through the temperature range prediction model to obtain an operating temperature prediction characteristic. The data processing device performs full joins and normalization on the operating temperature prediction characteristic through the first prediction head of the temperature range prediction model to obtain a reference operating temperature range for the tunneling assembly. The data processing device performs full joins and normalization on the operating temperature prediction characteristic through the second prediction head of the temperature range prediction model to obtain a reference operating temperature range for the engine.

[0071] Among them, the first prediction head is used to predict the reference operating temperature range of the tunneling component, and the second prediction head is used to predict the reference operating temperature range of the engine. When training the temperature range prediction model, the corresponding labeled reference temperature range will be used to train the first prediction head and the second prediction head respectively.

[0072] In the second part, the data processing device determines the working vibration operation information of the tunneling equipment based on the vibration parameter set, the geological parameter and the working parameter.

[0073] In one possible implementation, the data processing device determines a reference vibration parameter range for the tunneling assembly and a reference vibration parameter range for the engine based on the geological parameters and the operating parameters. The data processing device determines component vibration operating information of the tunneling assembly based on the reference vibration parameter range for the tunneling assembly and the vibration parameters of the tunneling assembly. The data processing device determines engine vibration operating information of the engine based on the reference vibration parameter range for the engine and the vibration parameters of the engine of the tunneling equipment. The data processing device determines operating vibration operating information of the tunneling equipment based on the component vibration operating information and the engine vibration operating information.

[0074] The reference vibration parameter range is the range of vibration parameters under normal conditions. For example, if vibration parameters include vibration intensity and vibration frequency, the reference vibration parameter range includes a reference vibration intensity range and a reference vibration frequency range. Assuming the engine and tunneling assembly remain unchanged, there is a corresponding relationship between the tunneling assembly's operating parameters, geological parameters, and vibration parameters. The reference vibration parameter range, rather than the reference vibration parameter, is determined because the operating context may be affected by factors such as the tunneling assembly's operating angle. The reference vibration parameter range takes into account the impact of other factors on the vibration parameters.

[0075] For example, the data processing device inputs the geological parameter and the operating parameter into a vibration parameter range prediction model. The vibration parameter range prediction model then extracts features from the geological parameter and the operating parameter to obtain geological features corresponding to the geological parameter and operating features corresponding to the operating parameter. The data processing device then fuses the geological features and the operating features using the vibration parameter range prediction model to obtain a vibration parameter prediction feature. The data processing device maps the vibration parameter prediction feature using the vibration parameter range prediction model to obtain a reference vibration parameter range for the tunneling assembly and a reference vibration parameter range for the engine. If the vibration parameters of the tunneling assembly are within the reference vibration parameter range (vibration intensity is within the reference vibration intensity range, and vibration frequency is within the reference vibration frequency range), the data processing device determines the component vibration operation information of the tunneling assembly as first component vibration operation information. This first component vibration operation information indicates that the component vibration of the tunneling assembly is normal, i.e., has a high degree of rationality. If the vibration parameters of the tunneling assembly are not within the reference vibration parameter range for the tunneling assembly (the vibration intensity is not within the reference vibration intensity range, and / or the vibration frequency is not within the reference vibration frequency range), the data processing device determines the tunneling assembly vibration operation information as second component vibration operation information, indicating that the component vibration of the tunneling assembly is abnormal, i.e., its rationality is low. If the vibration parameters of the engine are within the reference vibration parameter range for the engine, the data processing device determines the engine vibration operation information of the engine as first engine vibration operation information, indicating that the engine vibration of the engine is normal, i.e., its rationality is high. If the vibration parameters of the engine are not within the reference vibration parameter range for the engine, the data processing device determines the engine vibration operation information of the engine as second engine vibration operation information, indicating that the engine vibration of the engine is abnormal, i.e., its rationality is low. The data processing device concatenates the component vibration operation information and the engine vibration operation information to obtain the operating vibration operation information of the tunneling equipment.

[0076] Among them, the vibration parameter range prediction model is a regression model, which is trained based on multiple sample geological parameters, multiple sample working parameters and corresponding annotated reference vibration parameter ranges. The embodiment of this application does not limit the structure and training method of the vibration parameter range prediction model.

[0077] In order to more clearly illustrate the technical solution provided by the above example, the method of obtaining two reference vibration parameter ranges by using the vibration parameter range prediction model in the above example is explained below.

[0078] In some embodiments, the data processing device inputs the geological parameter and the operating parameter into a vibration parameter range prediction model, and performs multiple full connections on the geological parameter and the operating parameter through the vibration parameter range prediction model to obtain geological characteristics corresponding to the geological parameter and operating characteristics corresponding to the operating parameter. The data processing device fuses the geological characteristics and the operating characteristics through the vibration parameter range prediction model to obtain a vibration parameter prediction characteristic. The data processing device performs full connections and normalization on the vibration parameter prediction characteristic through the first prediction head of the vibration parameter range prediction model to obtain a reference vibration parameter range for the tunneling assembly. The data processing device performs full connections and normalization on the vibration parameter prediction characteristic through the second prediction head of the vibration parameter range prediction model to obtain a reference vibration parameter range for the engine.

[0079] Among them, the first prediction head is used to predict the reference vibration parameter range of the tunneling component, and the second prediction head is used to predict the reference vibration parameter range of the engine. When training the vibration parameter range prediction model, the corresponding labeled reference vibration parameter range will be used to train the first prediction head and the second prediction head respectively.

[0080] Part 3: The data processing device determines the rock mass action operation information of the tunneling equipment based on the working temperature operation information and the working vibration operation information.

[0081] In a possible implementation, a data processing device combines the working temperature operation information and the working vibration operation information to obtain the rock mass action operation information of the tunneling equipment.

[0082] 303. The video includes an environmental video and a tunneling component video. The data processing device determines the tunneling component status information of the tunneling device based on the device operating parameters and the tunneling component video. The tunneling component video is used to record the working conditions of the tunneling component of the tunneling device.

[0083] The component status information of the tunneling component is used to describe the component status of the tunneling component.

[0084] In one possible embodiment, the equipment operating parameters include the tunneling assembly's operating parameters and a strain parameter set. A data processing device performs video recognition on the tunneling assembly video to obtain the surface state and connection state of the tunneling assembly. The surface state represents the surface condition of the tunneling assembly. Based on the operating parameters, the strain parameter set, the surface state, and the connection state, the data processing device determines tunneling assembly status information of the tunneling equipment. The strain parameter set includes strains at multiple locations on the tunneling assembly.

[0085] Among them, the component surface state is used to represent the surface condition of the tunneling component, and the component connection state is used to represent the connection status between multiple sub-components in the tunneling component.

[0086] In this implementation, video recognition is performed on the tunneling component video to determine the surface condition and component connection status of the tunneling component. The tunneling component status information is then determined using the operating parameters, strain parameter set, component surface condition, and component connection status. This information accurately reflects the status of the tunneling component.

[0087] In order to explain the above embodiment more clearly, the above embodiment will be described in several parts below.

[0088] In the first part, the data processing equipment performs video recognition on the tunneling component video to obtain the component surface status and component connection status of the tunneling component.

[0089] In one possible implementation, the data processing device inputs the tunneling assembly video into a first video recognition model, extracts features from the tunneling assembly video using the first video recognition model, and obtains first video features of the tunneling assembly video. The data processing device maps the first video features using the first video recognition model to obtain the surface status and connection status of the tunneling assembly.

[0090] For example, the data processing device inputs the tunneling component video into a first video recognition model. The first video recognition model then convolves multiple video frames of the tunneling component video to obtain video frame features for each frame. The data processing device then uses the first video recognition model to encode the video frame features of the multiple frames based on an attention mechanism to obtain first video features for the tunneling component video. The data processing device then uses the first classification head of the first video recognition model to fully connect and normalize the first video features to obtain the surface state of the tunneling component. The data processing device then uses the second classification head of the first video recognition model to fully connect and normalize the first video features to obtain the connection state of the tunneling component.

[0091] Among them, the surface state of the component is one of multiple candidate surface states, and the multiple candidate surface states include intact surface, slightly damaged surface, damaged surface, and cracked surface. The component connection state is one of multiple candidate connection states, and the multiple candidate connection states include intact connection, damaged connection, and disconnected connection. Accordingly, determining the component surface state and component connection state can be regarded as classification based on the first video feature. The first video recognition model not only has video recognition capabilities, but also has multi-target classification capabilities. The embodiment of the present application does not limit the structure and training method of the first video recognition model.

[0092] In order to more clearly illustrate the technical solution provided in the above example, the method of using the first sorting head in the above example to determine the surface state of the component is described below.

[0093] In some embodiments, the data processing device performs full-connection and normalization on the first video features using a first classification head of the first video recognition model to obtain a first probability set for the tunneling component. The first probability set includes multiple probabilities, each probability corresponding to a candidate surface state. The data processing device determines the candidate surface state corresponding to the highest probability in the first probability set as the component surface state.

[0094] It should be noted that the method of using the second classification head to determine the connection status of the component in the above example and the method of determining the surface status of the component belong to the same inventive concept. The implementation process can be found in the above description and will not be repeated here.

[0095] In the second part, the data processing equipment determines the state information of the tunneling component of the tunneling equipment based on the working parameters, the strain parameter set, the surface state of the component and the connection state of the component. The strain parameter set includes strains at multiple positions on the tunneling component.

[0096] In one possible implementation, the data processing device determines operating strain information of the tunneling assembly based on the operating parameters and the strain parameter set. The data processing device determines operating damage information of the tunneling assembly based on the surface state and the connection state. The data processing device determines tunneling assembly status information of the tunneling equipment based on the operating strain information and the operating damage information.

[0097] The tunneling component inevitably generates strain during operation. The operational strain information, determined by combining the operational parameters and the strain parameter set, can be used to determine whether the strain is reasonable, that is, whether there are any anomalies. The surface condition and connection status can reflect the damage of the tunneling component from different dimensions. Therefore, the operational damage information determined by combining the surface condition and connection status can be used to reflect the damage status of the tunneling component.

[0098] For example, the data processing device determines the degree of deformation of the tunneling assembly based on the multiple strains in the strain parameter set. Based on the degree of deformation and the operating parameters, the data processing device determines operating strain information for the tunneling assembly. The data processing device combines the surface state and the connection state to obtain operating damage information for the tunneling assembly. The data processing device combines the operating strain information and the operating damage information to obtain tunneling assembly status information for the tunneling equipment.

[0099] In order to more clearly illustrate the technical solution provided by the above example, the following describes how the data processing device in the above example determines the working strain information of the tunneling component based on the deformation degree and the working parameters.

[0100] In some embodiments, the data processing device considers the degree of deformation and the operating parameter to belong to a strain information determination model, and extracts features of the operating parameter using the strain information determination model to obtain an operating parameter feature of the operating parameter. The data processing device fuses the operating parameter feature and the degree of deformation using the strain information determination model to obtain an operating strain information determination feature. The data processing device maps the operating strain information determination feature using the strain information determination model to obtain the operating strain information.

[0101] The working strain information includes normal strain and abnormal strain. Accordingly, the strain information determination model is a binary classification model. The embodiment of the present application does not limit the structure and training method of the strain information determination model.

[0102] 304. The data processing device determines the excavation effect information of the excavation device based on the geological parameters and the environmental video, where the environmental video is used to record the excavation status of the excavation device in the tunnel.

[0103] The tunneling effect information is used to indicate the tunneling effect of the tunneling equipment in the underground mine.

[0104] In one possible implementation, the geological parameters include rock hardness, rock density, and rock porosity. The data processing device performs video recognition on the environmental video to obtain the size and shape of the rock mass being crushed in the excavation direction of the tunneling equipment. Based on the rock hardness, rock porosity, and rock size, the data processing device determines size crushing effect information for the crushed rock mass. Based on the rock density and rock shape, the data processing device determines shape crushing effect information for the crushed rock mass. Based on the size crushing effect information and the shape crushing effect information, the data processing device determines tunneling effect information for the tunneling equipment.

[0105] The size crushing effect information is used to describe the size crushing effect of the tunneling component on the rock mass, and the shape crushing effect information is used to describe the shape crushing effect of the tunneling component on the rock mass.

[0106] In this implementation, video recognition is performed on the surrounding video to determine the size and shape of the rock mass being crushed in the tunneling direction of the tunneling equipment. This information is combined with the rock hardness, porosity, and size to determine the size and shape of the crushed rock mass. Furthermore, this information is combined with the rock density and shape to determine the shape and shape of the crushed rock mass. This combined size and shape information is used to generate final tunneling effect information, resulting in highly accurate tunneling effect information.

[0107] In order to explain the above embodiment more clearly, the above embodiment will be described in several parts below.

[0108] In the first part, the data processing equipment performs video recognition on the environmental video to obtain the rock size and rock shape of the broken rock mass in the excavation direction of the excavation equipment.

[0109] The data processing device inputs the environmental video into a second video recognition model, extracts features from the environmental video using the second video recognition model, and obtains second video features from the environmental video. The data processing device maps the second video features using the second video recognition model to obtain the rock mass size and shape of the tunneling assembly.

[0110] For example, the data processing device inputs the environmental video into a second video recognition model, and uses the second video recognition model to perform convolution on multiple video frames of the environmental video to obtain video frame features for each frame. The data processing device uses the second video recognition model to encode the video frame features of the multiple video frames based on an attention mechanism to obtain second video features of the environmental video. The data processing device uses the first classification head of the second video recognition model to fully connect and normalize the second video features to obtain the rock mass dimensions of the tunneling assembly. The data processing device uses the second classification head of the second video recognition model to fully connect and normalize the second video features to obtain the rock mass shape of the tunneling assembly.

[0111] Among them, the rock mass size is one of multiple candidate surface states, and the multiple candidate surface states include intact surface, slightly damaged surface, damaged surface, and cracked surface. The rock mass shape is one of multiple candidate connection states, and the multiple candidate connection states include intact connection, damaged connection, and disconnected connection. Accordingly, determining the rock mass size and rock mass shape can be regarded as a classification based on the second video feature. The second video recognition model not only has video recognition capabilities, but also has multi-target classification capabilities. The embodiment of the present application does not limit the structure and training method of the second video recognition model.

[0112] In the second part, the data processing equipment determines the size crushing effect information of the crushed rock mass based on the hardness of the rock mass, the porosity of the rock mass and the size of the rock mass.

[0113] In one possible implementation, the data processing device determines a reference rock size range for the crushed rock mass based on the rock mass hardness and the rock mass porosity. The reference rock size range includes multiple reference rock mass sizes, with each reference rock mass size corresponding to a piece of reference size crushing effect information. The data processing device determines the reference size crushing effect information corresponding to the rock mass size within the reference rock size range as the size crushing effect information for the crushed rock mass.

[0114] The multiple reference rock sizes and the corresponding reference size crushing effect information are set by technicians according to actual conditions, and the embodiments of the present application do not limit this. In some embodiments, the reference size crushing effect information is good effect, average effect and poor effect.

[0115] The third part, data processing equipment determines the shape crushing effect information of the crushed rock mass based on the density and shape of the rock mass.

[0116] In one possible implementation, a data processing device determines a reference rock shape set for the crushed rock mass based on the rock mass density. The reference rock shape set includes multiple reference rock shapes, each corresponding to a piece of reference shape crushing effect information. The data processing device determines the reference shape crushing effect information corresponding to the rock shape in the reference rock shape set as the shape crushing effect information for the crushed rock mass.

[0117] The multiple reference rock shapes and the corresponding reference shape crushing effect information are set by technicians according to actual conditions, and the embodiments of the present application do not limit this. In some embodiments, the reference shape crushing effect information is good effect, average effect and poor effect.

[0118] Part 4: The data processing device determines the excavation effect information of the excavation device based on the size crushing effect information and the shape crushing effect information.

[0119] In a possible implementation, a data processing device fuses the size crushing effect information and the shape crushing effect information to obtain the tunneling effect information of the tunneling device.

[0120] 305. The data processing device determines the environmental effect operation state of the tunneling equipment based on the rock mass operation information, the tunneling component status information, and the tunneling effect information. The environmental effect operation state is used to represent the operation status of the tunneling equipment under the influence of the external environment.

[0121] The external environment refers to the rock mass in the direction of excavation, and the external environment refers to the interaction between the rock mass and the excavation components of the excavation equipment. When excavating different types of rock mass, the effects of the interaction between the excavation components and the rock mass vary. In some embodiments, the operating state of the environmental effect is represented by environmental state description information, which describes the operating state of the environmental effect in the form of text or latent vectors.

[0122] In one possible implementation, a data processing device combines the rock mass effect operation information, the tunneling component status information, and the tunneling effect information to obtain first operation status determination information. The data processing device inputs the first operation status determination information into an environmental effect operation status determination model, and uses the environmental effect operation status determination model to extract features from the first operation status determination information to obtain a first operation status determination feature. The data processing device decodes the first operation status determination feature using the environmental effect operation status determination model to obtain the environmental effect operation status of the tunneling equipment.

[0123] The environment effect running state determination model is a regression model, which can map the first running state determination information into the environment effect running state.

[0124] For example, the data processing device concatenates the rock mass action operation information, the tunneling component status information, and the tunneling effect information to obtain first operation state determination information. The data processing device then inputs this first operation state determination information into an environmental action operation state determination model. The environmental action operation state determination model then performs multiple full connections on the first operation state determination information to obtain a first operation state determination feature. The data processing device then uses the environmental action operation state determination model, using an attention mechanism, to perform multiple rounds of iterative decoding on the first operation state determination feature to obtain the environmental action operation state of the tunneling equipment.

[0125] 306. The data processing device determines the physiological status information of the operator in the tunneling device based on the device operating parameters and the physiological parameters.

[0126] Among them, the physiological state information is used to describe the physiological state of the operator. The physiological state information is obtained by considering the cabin environment and physiological parameters of the personnel cabin. Compared with the physiological state information obtained by only considering the physiological parameters, it is more consistent with the actual situation.

[0127] In one possible embodiment, the equipment operating parameters include cabin environmental parameters. The data processing device determines environmental description information of the personnel cabin of the tunneling equipment based on the cabin environmental parameters. The cabin environmental parameters include cabin vibration data, cabin temperature, cabin pressure, and cabin gas content within the personnel cabin. The data processing device determines physiological status information of the operator based on the environmental description information and the physiological parameters.

[0128] For example, the data processing device inputs the cabin vibration data, cabin temperature, cabin pressure, and cabin gas content into an environmental description information generation model. The environmental description information generation model then extracts features from the cabin vibration data, cabin temperature, cabin pressure, and cabin gas content to obtain cabin environmental features. The data processing device decodes the cabin environmental features using the environmental description information generation model to obtain the environmental description information. The data processing device inputs the environmental description information and the physiological parameters into a physiological state information determination model. The physiological state information determination model then extracts features from the environmental description information and the physiological parameters to obtain environmental description features of the environmental description information and physiological features of the physiological parameters. The data processing device fuses the environmental description features and the physiological features using the physiological state information determination model to obtain environmental physiological features. The data processing device decodes the environmental physiological features using the physiological state information determination model to obtain physiological state information of the operator.

[0129] In order to more clearly illustrate the implementation method provided by the above example, the following describes the method of determining a model through the physiological state information in the above example, decoding the physiological characteristics of the environment, and obtaining the physiological state information of the operator.

[0130] In some embodiments, the data processing device uses the physiological state determination model to perform multiple rounds of iterative decoding on the physiological characteristics of the environment based on the attention mechanism to obtain the physiological state information of the operator.

[0131] 307. The data processing device determines operation state information of the operator based on the physiological state information and the operation set, where the operation state information is used to indicate the degree of controllability of the operator over the executed operation.

[0132] In one possible implementation, the data processing device determines the operator's level of physiological abnormality based on the physiological state information and the operator's reference physiological state information. The reference physiological state information is determined based on the equipment operating parameters and the operator's reference physiological parameters, which are the average physiological parameters of the operator when using the tunneling equipment. The data processing device determines the operator's level of operational abnormality based on the operation set and the operator's operating pattern, where the operating pattern represents the operator's operating habits. The data processing device determines the operator's operational state information based on the operator's level of physiological abnormality and the level of operational abnormality.

[0133] The reference physiological state information can be considered a baseline for the operator's physiological state. The method for determining the reference physiological state information based on the device operating parameters and the reference physiological parameters is consistent with the method for determining the physiological state information in step 306 , and the implementation process is not further described. The operating mode represents the operator's operating habits. Using the operating mode, abnormalities in the operator's operations can be identified. That is, under normal circumstances, the operator's operating habits will not change. However, any operation that violates these habits may indicate an abnormal situation.

[0134] In order to explain the above embodiment more clearly, the above embodiment will be described in several parts below.

[0135] The first part is that a data processing device determines the degree of physiological abnormality of the operator based on the physiological state information and the reference physiological state information of the operator.

[0136] In one possible implementation, a data processing device performs feature extraction on the physiological state information and the reference physiological state information to obtain physiological state features of the physiological state information and reference physiological state features of the reference physiological state information. The data processing device determines feature similarity between the physiological state features and the reference physiological state features, and determines the degree of physiological abnormality of the operator based on the feature similarity.

[0137] The feature similarity is negatively correlated with the degree of physiological abnormality, that is, the higher the feature similarity, the lower the degree of physiological abnormality; and the lower the feature similarity, the higher the degree of physiological abnormality. In some embodiments, the reciprocal of the feature similarity is determined as the degree of physiological abnormality.

[0138] In the second part, the data processing device determines the abnormality degree of the operator's operation based on the operation set and the operator's operation mode.

[0139] Among them, the operation set includes multiple operations on the tunneling equipment, the operation mode includes multiple groups of operation sequences, one group of operation sequences includes multiple operations, and one group of operation sequences is a combination of multiple operations commonly used by operators. The multiple groups of operation sequences can reflect the operating habits of the operators.

[0140] In one possible implementation, a data processing device performs feature extraction on the operation set and the operation mode to obtain operation features of the operation set and operation mode features of the operation mode. The data processing device determines feature similarity between the operation features and the operation mode features, and determines the degree of abnormality of the operator's operation based on the feature similarity.

[0141] The feature similarity is negatively correlated with the degree of abnormal operation, that is, the higher the feature similarity, the lower the degree of abnormal operation; and the lower the feature similarity, the higher the degree of abnormal operation. In some embodiments, the reciprocal of the feature similarity is determined as the degree of abnormal operation.

[0142] Part 3: The data processing device determines the operation status information of the operator based on the degree of physiological abnormality and the degree of operation abnormality of the operator.

[0143] In a possible implementation, the data processing device combines the operator's physiological abnormality level and operational abnormality level to obtain the operator's operational status information.

[0144] 308. The data processing device determines operation matching information of the tunneling device based on the device operating parameters and the operation set.

[0145] In one possible embodiment, the equipment operating parameters include movement parameters of the tunneling equipment and operating parameters of the tunneling assembly. The operation set includes a movement operation set and a tunneling operation set. The data processing device determines movement operation matching information for the tunneling equipment based on the movement parameters and the movement operation set. The movement operation matching information indicates a match between the movement operation and the movement parameters, and the movement operation is used to control the movement of the tunneling equipment. The data processing device determines tunneling operation matching information for the tunneling equipment based on the operating parameters and the tunneling operation set. The tunneling operation matching information indicates a match between the tunneling operation and the operating parameters, and the tunneling operation is used to control the tunneling assembly. The data processing device determines operation matching information for the tunneling equipment based on the movement operation matching information and the tunneling operation matching information.

[0146] Among them, the operation matching information is used to reflect the degree of matching between the operating conditions of the tunneling equipment and the operations of the operator. Generally speaking, the higher the matching degree, the better the controllability of the tunneling equipment and the lower the probability of failure; correspondingly, the lower the matching degree, the worse the controllability of the tunneling equipment and the higher the probability of failure.

[0147] In this embodiment, movement parameters and a movement operation set are used to determine movement operation matching information, while working parameters and a tunneling operation set are used to determine tunneling operation matching information. Combining the movement operation matching information with the tunneling operation matching information allows for determining operation matching information, thereby determining the degree of matching between the operation and the operating status of the tunneling equipment.

[0148] In order to explain the above embodiment more clearly, the above embodiment will be described in several parts below.

[0149] In the first part, a data processing device determines movement operation matching information of the tunneling device based on the movement parameter and the movement operation set.

[0150] The movement parameters include movement speed and movement direction, the movement operation set includes multiple movement operations, and the movement operations are used to control the movement speed and movement direction of the tunneling equipment.

[0151] In one possible implementation, the data processing device determines reference movement parameters of the tunneling device based on the movement operation set, where the reference movement parameters correspond to the movement operation set. The data processing device determines movement operation matching information of the tunneling device based on the reference movement parameters and the movement parameters.

[0152] The reference movement parameter is an expected movement parameter after the set of movement operations is performed on the tunneling equipment.

[0153] In this embodiment, the movement operation set is used to determine the reference movement parameters of the tunneling equipment, and the reference movement parameters and the movement parameters are used to determine the movement operation matching information, which has a high accuracy.

[0154] For example, the data processing device inputs the movement operation set into a movement parameter prediction model, performs feature extraction on the movement operation set using the movement parameter prediction model, and obtains operation set features of the movement operation set. The data processing device maps the operation set features using the movement parameter prediction model to obtain the reference movement parameters. The data processing device determines the movement operation matching information based on the difference information between the reference movement parameters and the movement parameters.

[0155] The difference information indicates the degree of difference between the reference movement parameters and the movement parameters, and the movement operation matching information indicates the degree of match between the movement operation set and the movement parameters. Generally speaking, the degree of difference is negatively correlated with the degree of match; that is, the higher the degree of difference, the lower the degree of match; the lower the degree of difference, the higher the degree of match. The movement parameter determination model is a regression model, obtained through multiple rounds of training based on multiple sample movement operation sets and the annotated movement parameters corresponding to each sample movement operation set. The present embodiment does not limit the structure and training method of the movement parameter determination model.

[0156] In the second part, the data processing device determines the tunneling operation matching information of the tunneling device based on the working parameters and the tunneling operation set.

[0157] The working parameters include rotational speed and torque, the tunneling operation set includes multiple tunneling operations, and the tunneling operations are used to control the rotational speed and torque of the tunneling component.

[0158] In one possible embodiment, the data processing device determines reference operating parameters of the tunneling component based on the tunneling operation set, where the reference operating parameters correspond to the tunneling operation set. The data processing device determines tunneling operation matching information for the tunneling component based on the reference operating parameters and the operating parameters.

[0159] The reference operating parameters are expected operating parameters after the tunneling component performs the tunneling operation set.

[0160] In this embodiment, the excavation operation set is used to determine the reference working parameters of the excavation component, and the excavation operation matching information is determined using the reference working parameters and the working parameters, so that the excavation operation matching information has high accuracy.

[0161] For example, the data processing device inputs the excavation operation set into a working parameter prediction model, performs feature extraction on the excavation operation set using the working parameter prediction model, and obtains operation set features for the excavation operation set. The data processing device maps the operation set features using the working parameter prediction model to obtain the reference working parameters. Based on the difference information between the reference working parameters and the working parameters, the data processing device determines matching information for the excavation operation.

[0162] The difference information indicates the degree of difference between the reference operating parameters and the operating parameters, and the tunneling operation matching information indicates the degree of match between the tunneling operation set and the operating parameters. Generally speaking, the degree of difference is negatively correlated with the degree of match; that is, the higher the degree of difference, the lower the degree of match; the lower the degree of difference, the higher the degree of match. The operating parameter determination model is a regression model, obtained through multiple rounds of training based on multiple sample tunneling operation sets and the labeled operating parameters corresponding to each sample tunneling operation set. This embodiment of the application does not limit the structure and training method of the operating parameter determination model.

[0163] Part three: The data processing device determines the operation matching information of the tunneling device based on the movement operation matching information and the tunneling operation matching information.

[0164] In a possible implementation, the data processing device combines the movement operation matching information and the tunneling operation matching information to obtain the operation matching information of the tunneling device.

[0165] 309. The data processing device determines a human-machine interaction state of the tunneling device based on the physiological state information, the operation state information, and the operation matching information. The human-machine interaction state is used to represent the interaction between the operator and the tunneling device.

[0166] When an operator operates the tunneling equipment, the operator's operation will affect the operating state of the tunneling equipment. Accordingly, due to the high power and vibration intensity of the tunneling equipment, the tunneling equipment has a greater impact on the operator. In the event of an abnormality in the operating state of the tunneling equipment, the impact on the operator will also change. This change can be represented by the operator's physiological state. Therefore, by determining the operator's physiological state, the operating state of the tunneling equipment can also be inferred. In some embodiments, the human-machine interaction state is represented by human-machine state description information, which is information that describes the human-machine interaction state in the form of text or latent vectors.

[0167] In one possible implementation, the data processing device determines the operator's status based on the physiological status information and the operational status information. The data processing device determines the operational status of the tunneling equipment based on the operational matching information. The data processing device determines the human-machine interaction state of the tunneling equipment based on the operator's status and the operational status.

[0168] The personnel status indicates the operator's status under the influence of the tunneling equipment. For example, personnel status includes normal, slightly abnormal, and abnormal. The operation status indicates the response status to the operation. For example, the operation status includes timely response, delayed response, and abnormal response.

[0169] For example, the data processing device concatenates the physiological state information and the operational state information to obtain personnel state determination information. The data processing device inputs the personnel state determination information into a personnel state determination model, and uses the personnel state determination model to extract features from the personnel state determination information to obtain personnel state determination features. The data processing device decodes the personnel state determination features using the personnel state determination model to obtain the personnel state of the operator. The data processing device inputs the operation matching information into an operational state determination model, and uses the operational state determination model to extract features from the operation matching information to obtain operational state determination features. The data processing device decodes the operational state determination features using the operational state determination model to obtain the operational state of the tunneling equipment. The data processing device concatenates the personnel state and the operational state to obtain the human-machine interaction state of the tunneling equipment.

[0170] 310. The data processing device determines the equipment operation state of the tunneling equipment based on the environmental operation state of the tunneling equipment and the human-machine operation state.

[0171] The device operating status represents the operating status of the tunneling equipment, and can be used to determine whether there are any abnormalities in the operation of the tunneling equipment. In some embodiments, the device operating status is represented by device operating status description information, which is information describing the device operating status in the form of text or latent vectors.

[0172] In a possible implementation, a data processing device combines the environmental action operating status and the human-machine action status of the tunneling device to obtain the device operating status of the tunneling device.

[0173] In some embodiments, when the operating status of the equipment meets the preset conditions, it means that there is no abnormality in the operation of the tunneling equipment; when the operating status of the equipment does not meet the preset conditions, it means that there is an abnormality in the operation of the tunneling equipment. The preset conditions are set by technical personnel according to actual conditions, and the embodiments of this application do not limit this.

[0174] All of the above optional technical solutions can be combined in any way to form optional embodiments of the present application, and will not be described in detail here.

[0175] Through the technical solution provided in the embodiment of the present application, when the tunneling equipment is working, the equipment operating parameters of the tunneling equipment, the geological parameters of the underground mine in which it is located, the video in the tunneling direction, and the physiological parameters and operation set of the operator in the tunneling equipment are obtained. The equipment operating parameters, geological parameters and the video are used to determine the environmental action operating state of the tunneling equipment, thereby obtaining the operating conditions of the tunneling equipment under the influence of the external environment. The equipment operating parameters, physiological parameters and the operation set are used to determine the human-machine action state of the tunneling equipment, thereby obtaining the interaction between the operator and the tunneling equipment. In combination with the environmental action operating state and the human-machine action state, the equipment operating state of the tunneling equipment is determined, and the equipment operating state of the tunneling equipment is evaluated during the working process, abnormalities of the tunneling equipment are discovered in a timely manner, and the safety of operations using the tunneling equipment is improved.

[0176] Figure 4 This is a schematic diagram of the structure of an operating status evaluation device for underground mine tunneling equipment provided in an embodiment of the present application, see Figure 4 The device includes: an acquisition module 401, an environment action operation state determination module 402, a human-machine action state determination module 403 and an equipment operation state determination module 404.

[0177] The acquisition module 401 is used to obtain the equipment operating parameters of the tunneling equipment, the geological parameters of the underground mine where the tunneling equipment is located, the video of the tunneling direction of the tunneling equipment, and the physiological parameters and operation set of the operator in the tunneling equipment when the tunneling equipment is working.

[0178] The environmental effect operating state determination module 402 is used to determine the environmental effect operating state of the tunneling equipment based on the equipment operating parameters, the geological parameters and the video. The environmental effect operating state is used to represent the operating status of the tunneling equipment under the influence of the external environment.

[0179] The human-machine interaction state determination module 403 is used to determine the human-machine interaction state of the tunneling equipment based on the equipment operating parameters, the physiological parameters and the operation set. The human-machine interaction state is used to represent the interaction between the operator and the tunneling equipment.

[0180] The equipment operation state determination module 404 is used to determine the equipment operation state of the tunneling equipment based on the environmental operation state of the tunneling equipment and the human-machine operation state.

[0181] In one possible embodiment, the video includes an environmental video and a tunneling component video. The environmental effect operating status determination module 402 is configured to determine rock effect operating information of the tunneling equipment based on the equipment operating parameters and the geological parameters. Based on the equipment operating parameters and the tunneling component video, tunneling component status information of the tunneling equipment is determined. The tunneling component video is used to record the operating status of the tunneling component of the tunneling equipment. Based on the geological parameters and the environmental video, tunneling performance information of the tunneling equipment is determined. The environmental video is used to record the tunneling performance of the tunneling equipment within the tunnel. The environmental effect operating status of the tunneling equipment is determined based on the rock effect operating information, the tunneling component status information, and the tunneling performance information.

[0182] In one possible embodiment, the equipment operating parameters include a temperature parameter set, a vibration parameter set, and operating parameters of the tunneling assembly. The environmental effect operating state determination module 402 is configured to determine operating temperature information of the tunneling equipment based on the temperature parameter set, the geological parameters, and the operating parameters. The temperature parameter set includes the operating temperature of the tunneling assembly and the operating temperature of the engine of the tunneling equipment. Based on the vibration parameter set, the geological parameters, and the operating parameters, the module determines operating vibration information of the tunneling equipment. The vibration parameter set includes the vibration parameters of the tunneling assembly and the vibration parameters of the engine. Based on the operating temperature information and the operating vibration information, the module determines rock mass effect operating information of the tunneling equipment.

[0183] In one possible embodiment, the equipment operating parameters include the tunneling component's operating parameters and a strain parameter set. The environmental effect operating state determination module 402 is configured to perform video recognition on the tunneling component video to obtain the component surface state and component connection state of the tunneling component. The component surface state represents the surface condition of the tunneling component. Based on the operating parameters, the strain parameter set, the component surface state, and the component connection state, the tunneling component state information of the tunneling equipment is determined. The strain parameter set includes strains at multiple locations on the tunneling component.

[0184] In one possible implementation, the geological parameters include rock hardness, rock density, and rock porosity. The environmental action operating state determination module 402 is configured to perform video recognition on the environmental video to determine the size and shape of the rock mass being crushed in the excavation direction of the tunneling equipment. Based on the rock hardness, rock porosity, and rock size, size crushing effect information for the crushed rock mass is determined. Based on the rock density and rock shape, shape crushing effect information for the crushed rock mass is determined. Based on the size crushing effect information and the shape crushing effect information, tunneling effect information for the tunneling equipment is determined.

[0185] In one possible implementation, the human-machine interaction state determination module 403 is configured to determine physiological state information of an operator within the tunneling equipment based on the equipment operating parameters and the physiological parameters. Based on the physiological state information and the operation set, the operator's operational state information is determined, where the operational state information indicates the operator's controllability over the executed operation. Based on the equipment operating parameters and the operation set, operational matching information for the tunneling equipment is determined. Based on the physiological state information, the operational state information, and the operational matching information, the human-machine interaction state of the tunneling equipment is determined.

[0186] In one possible embodiment, the equipment operating parameters include cabin environmental parameters. The human-machine interaction state determination module 403 is configured to determine environmental description information of the personnel cabin of the tunneling equipment based on the cabin environmental parameters. The cabin environmental parameters include cabin vibration data, cabin temperature, cabin pressure, and cabin gas content within the personnel cabin. Based on the environmental description information and the physiological parameters, the operator's physiological state information is determined.

[0187] In one possible embodiment, the equipment operating parameters include movement parameters of the tunneling equipment and operating parameters of the tunneling assembly. The operation set includes a movement operation set and a tunneling operation set. The human-machine action state determination module 403 is configured to determine movement operation matching information of the tunneling equipment based on the movement parameters and the movement operation set. The movement operation matching information indicates a match between the movement operation and the movement parameters, and the movement operation is used to control the movement of the tunneling equipment. Based on the operating parameters and the tunneling operation set, tunneling operation matching information of the tunneling equipment is determined. The tunneling operation matching information indicates a match between the tunneling operation and the operating parameters, and the tunneling operation is used to control the tunneling assembly. Based on the movement operation matching information and the tunneling operation matching information, operation matching information of the tunneling equipment is determined.

[0188] In one possible implementation, the human-machine interaction state determination module 403 is configured to determine the operator's degree of physiological abnormality based on the physiological state information and the operator's reference physiological state information. The reference physiological state information is determined based on the equipment operating parameters and the operator's reference physiological parameters, which are the average physiological parameters of the operator when using the tunneling equipment. The operator's degree of operational abnormality is determined based on the operation set and the operator's operating mode, where the operating mode represents the operator's operating habits. The operator's operational state information is determined based on the operator's degree of physiological abnormality and the degree of operational abnormality.

[0189] In one possible implementation, the human-machine interaction state determination module 403 is configured to determine the operator's state based on the physiological state information and the operational state information. Based on the operational matching information, the operational state of the tunneling equipment is determined. Based on the operator's state and the operational state, the human-machine interaction state of the tunneling equipment is determined.

[0190] It should be noted that the operating status evaluation device for underground mine tunneling equipment provided in the above embodiment only uses the division of the above-mentioned functional modules as an example to illustrate when evaluating the operating status of the tunneling equipment. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the personnel structure of the computer equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the operating status evaluation device for underground mine tunneling equipment provided in the above embodiment and the operating status evaluation method embodiment of underground mine tunneling equipment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0191] Through the technical solution provided in the embodiment of the present application, when the tunneling equipment is working, the equipment operating parameters of the tunneling equipment, the geological parameters of the underground mine in which it is located, the video in the tunneling direction, and the physiological parameters and operation set of the operator in the tunneling equipment are obtained. The equipment operating parameters, geological parameters and the video are used to determine the environmental action operating state of the tunneling equipment, thereby obtaining the operating conditions of the tunneling equipment under the influence of the external environment. The equipment operating parameters, physiological parameters and the operation set are used to determine the human-machine action state of the tunneling equipment, thereby obtaining the interaction between the operator and the tunneling equipment. In combination with the environmental action operating state and the human-machine action state, the equipment operating state of the tunneling equipment is determined, and the equipment operating state of the tunneling equipment is evaluated during the working process, abnormalities of the tunneling equipment are discovered in a timely manner, and the safety of operations using the tunneling equipment is improved.

[0192] Figure 5 This is a structural diagram of a computer device provided in an embodiment of the present application. The computer device can be implemented as the above-mentioned data processing device. The computer device 500 may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) 501 and one or more memories 502, wherein the one or more memories 502 store at least one computer program, and the at least one computer program is loaded and executed by the one or more processors 501 to implement the methods provided in the above-mentioned various method embodiments. Of course, the computer device 500 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output. The computer device 500 may also include other components for implementing device functions, which will not be described in detail here.

[0193] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including a computer program. The computer program can be executed by a processor to implement the method for evaluating the operating status of underground mine tunneling equipment in the above-described embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, or an optical parameter storage device.

[0194] In an exemplary embodiment, a computer program product or computer program is also provided, which includes a program code, which is stored in a computer-readable storage medium. The processor of a computer device reads the program code from the computer-readable storage medium, and the processor executes the program code, so that the computer device executes the above-mentioned underground mine tunnel excavation equipment operation status assessment method.

[0195] In some embodiments, the computer program involved in the embodiments of the present application may be deployed and executed on a computer device, or on multiple computer devices located at one location, or on multiple computer devices distributed at multiple locations and interconnected through a communication network. Multiple computer devices distributed at multiple locations and interconnected through a communication network may constitute a blockchain system.

[0196] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or may be accomplished by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0197] The above are only optional embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for evaluating the operating status of underground mine tunneling equipment, characterized in that: The method comprises: When the tunneling equipment is in operation, obtaining equipment operating parameters of the tunneling equipment, geological parameters of the underground mine in which the tunneling equipment is located, video of the tunneling equipment in the tunneling direction, and a set of physiological parameters and operations of an operator within the tunneling equipment, the video including environmental video and tunneling component video; determining an environmental operating state of the tunneling equipment based on the equipment operating parameters, the geological parameters, and the video, wherein the environmental operating state is used to indicate an operating condition of the tunneling equipment under an external environment; Determining the environmental effect operating state of the tunneling equipment based on the equipment operating parameters, the geological parameters, and the video includes: determining, based on the equipment operating parameters and the geological parameters, rock mass effect operating information of the tunneling equipment; determining, based on the equipment operating parameters and the tunneling component video, tunneling component status information of the tunneling equipment, the tunneling component video being used to record the working condition of the tunneling component of the tunneling equipment; determining, based on the geological parameters and the environmental video, tunneling effect information of the tunneling equipment, the environmental video being used to record the tunneling condition of the tunneling equipment in the tunnel; determining, based on the rock mass effect operating information, the tunneling component status information, and the tunneling effect information, the environmental effect operating status of the tunneling equipment; determining a human-machine interaction state of the tunneling equipment based on the equipment operating parameters, the physiological parameters, and the operation set, wherein the human-machine interaction state is used to represent the interaction between the operator and the tunneling equipment; The equipment operating state of the tunneling equipment is determined based on the environmental effect operating state and the human-machine effect state of the tunneling equipment.

2. The method according to claim 1, characterized in that The equipment operating parameters include a temperature parameter set, a vibration parameter set, and operating parameters of the tunneling assembly. Determining the rock mass action operating information of the tunneling equipment based on the equipment operating parameters and the geological parameters includes: Determining operating temperature information of the tunneling equipment based on the temperature parameter set, the geological parameter, and the operating parameter, the temperature parameter set including the operating temperature of the tunneling assembly and the operating temperature of the engine of the tunneling equipment; Determining operating vibration information of the tunneling equipment based on the vibration parameter set, the geological parameters, and the operating parameters, the vibration parameter set including vibration parameters of the tunneling assembly and vibration parameters of the engine; Based on the working temperature operating information and the working vibration operating information, rock mass action operating information of the tunneling equipment is determined.

3. The method according to claim 1, characterized in that The equipment operating parameters include the working parameters and strain parameter set of the tunneling component, and determining the tunneling component status information of the tunneling equipment based on the equipment operating parameters and the tunneling component video includes: Performing video recognition on the tunneling component video to obtain a component surface state and a component connection state of the tunneling component, wherein the component surface state is used to represent a surface condition of the tunneling component; The tunneling component state information of the tunneling equipment is determined based on the working parameters, the strain parameter set, the component surface state and the component connection state, wherein the strain parameter set includes strains at multiple positions on the tunneling component.

4. The method according to claim 1, wherein The geological parameters include rock hardness, rock density, and rock porosity. Determining the tunneling effect information of the tunneling equipment based on the geological parameters and the environmental video includes: Performing video recognition on the environmental video to obtain the rock mass size and rock mass shape of the broken rock mass in the excavation direction of the excavation equipment; Determining size crushing effect information of the crushed rock mass based on the hardness of the rock mass, the porosity of the rock mass, and the size of the rock mass; Determining shape crushing effect information of the crushed rock mass based on the rock mass density and the rock mass shape; Based on the size crushing effect information and the shape crushing effect information, excavation effect information of the excavation equipment is determined.

5. The method according to claim 1, characterized in that The determining of the human-machine interaction state of the tunneling equipment based on the equipment operating parameters, the physiological parameters, and the operation set includes: determining physiological status information of an operator in the tunneling equipment based on the equipment operating parameters and the physiological parameters; Determining operation state information of the operator based on the physiological state information and the operation set, wherein the operation state information is used to indicate a degree of controllability of the operator over the performed operation; Determining operation matching information of the tunneling equipment based on the equipment operating parameters and the operation set; The human-machine interaction state of the tunneling equipment is determined based on the physiological state information, the operation state information and the operation matching information.

6. The method according to claim 5, characterized in that The equipment operating parameters include cabin environment parameters, and determining the physiological state information of the operator in the tunneling equipment based on the equipment operating parameters and the physiological parameters includes: Determining environmental description information of a personnel cabin of the tunneling equipment based on the cabin environmental parameters, wherein the cabin environmental parameters include cabin vibration data, cabin temperature, cabin pressure, and cabin gas content in the personnel cabin; Based on the environmental description information and the physiological parameters, the physiological state information of the operator is determined.

7. The method according to claim 5, characterized in that The equipment operating parameters include movement parameters of the tunneling equipment and working parameters of the tunneling assembly, the operation set includes a movement operation set and a tunneling operation set, and determining operation matching information of the tunneling equipment based on the equipment operating parameters and the operation set includes: Determining, based on the movement parameters and the movement operation set, movement operation matching information of the tunneling device, wherein the movement operation matching information is used to indicate a matching condition between the movement operation and the movement parameters, and the movement operation is used to control the movement of the tunneling device; determining, based on the operating parameters and the tunneling operation set, tunneling operation matching information of the tunneling equipment, the tunneling operation matching information being used to indicate a matching condition between a tunneling operation and the operating parameters, the tunneling operation being used to control a tunneling component of the tunneling equipment; Based on the movement operation matching information and the excavation operation matching information, operation matching information of the excavation equipment is determined.

8. The method according to claim 5, characterized in that The determining, based on the physiological state information and the operation set, the operation state information of the operator includes: determining a degree of physiological abnormality of the operator based on the physiological state information and reference physiological state information of the operator, wherein the reference physiological state information is determined based on the equipment operating parameters and reference physiological parameters of the operator, the reference physiological parameters being average physiological parameters of the operator when using the tunneling equipment; determining a degree of abnormality in the operation of the operator based on the operation set and the operation mode of the operator, wherein the operation mode is used to represent the operation habits of the operator; Based on the degree of physiological abnormality and the degree of operational abnormality of the operator, the operation status information of the operator is determined.

9. The method according to claim 5, characterized in that The determining of the human-machine interaction state of the tunneling equipment based on the physiological state information, the operation state information, and the operation matching information includes: determining the operator's status based on the physiological status information and the operational status information; determining an operating state of the tunneling equipment based on the operation matching information; Based on the personnel status and the operation status, the human-machine interaction status of the tunneling equipment is determined.

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