Digital twinborn monitoring method and system for power unit of underground robot

Abstract

The invention specifically discloses a digital twinning monitoring method and system for a power unit of an underground robot, and relates to the technical field of intelligent monitoring and digital twinning of mining equipment. The method comprises the following steps: S1, carrying out multi-modal data acquisition on a battery entity; s2, pre-processing the multi-modal data and transmitting the pre-processed multi-modal data into a digital twinborn space; s3, constructing a twin model of the battery and carrying out physical modeling on the transmitted data; s4, deducing the health state of the battery, and predicting the future health state; s5, monitoring the gas concentration when the underground robot works; s6, when the health state prediction value and the gas concentration of the battery are about to exceed the safety threshold values, early warning prompt is carried out; and S7, after early warning occurs, all safety elements are controlled to work, and the safety of underground work of the underground robot is guaranteed. According to the method, digital twinning and multi-modal large model technologies are fused, the monitoring efficiency and accuracy are improved, and reliable guarantee is provided for operation safety of the underground robot.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring and digital twin technology of mining equipment, and in particular to a digital twin monitoring method and system for an underground robot power unit. Background Art

[0002] In the field of intelligent underground operations, real-time status monitoring of underground robot power units is a core technical component for ensuring reliable equipment operation and operational safety. Existing power unit monitoring systems generally employ state-of-charge (SOC) estimation methods based on equivalent circuit models, combined with preset threshold alarm mechanisms for basic early warning functions, and rely on limited temperature monitoring systems using thermocouples or infrared sensors. While these monitoring solutions can provide basic perception of some power unit parameters, they suffer from fundamental limitations in multi-physics field coupling analysis and real-time response capabilities.

[0003] The operation of a downhole robot's power unit is essentially a complex dynamic process involving the coupling and synergistic interaction of multiple physical fields, including electric, thermal, and mechanical fields. However, existing monitoring technologies often employ a single-field independent analysis model, lacking systematic modeling of the multi-field interaction mechanisms. For example, during battery charging and discharging, the heat generation and thermal field distribution caused by changes in the electric field can in turn affect the battery's electrochemical performance. However, traditional methods lack an effective coupling model, resulting in significant bias and inaccuracy in the assessment of the power unit's operating status, making it impossible to truly reflect the equipment's actual operating conditions.

[0004] At the same time, existing monitoring systems utilize a discrete sensor data acquisition architecture and simple threshold determination logic. The data processing process involves multiple steps, including acquisition, transmission, parsing, and analysis, resulting in inherent delays in system response. When the power unit experiences abnormal operating conditions such as a sudden drop in battery voltage or localized overheating, the monitoring system is unable to identify the status and issue warnings within milliseconds. This lag in response can easily lead to delayed fault handling, especially in the complex underground environment of high temperature, high humidity, and strong electromagnetic interference. This can lead to serious accidents such as battery thermal runaway and equipment downtime, severely restricting the advancement of intelligent underground robot operations.

[0005] As underground space development expands to deeper and more complex areas, the shortcomings of existing monitoring technologies in multi-physical field collaborative analysis capabilities and real-time response efficiency have become a technical bottleneck hindering the safe and reliable operation of underground robots. It is urgent to propose new monitoring theories and technical solutions to meet the development needs of underground intelligent operation equipment. Summary of the Invention

[0006] The purpose of this invention is to propose a digital twin monitoring method and system for the power unit of an underground robot. By collecting battery data through multiple sensors, constructing a multi-physics field coupling model, and integrating multimodal large-scale model analysis techniques, this method enables efficient and accurate monitoring and trend prediction of the power unit's status. Furthermore, gas sensors are used to ensure a safe operating environment, and combined with early warning and emergency response modules, this method ensures the stable operation of the underground robot under complex working conditions.

[0007] To achieve the above objectives, the present invention proposes a digital twin monitoring method for the power unit of an underground robot, which comprises the following steps: Step S1, performing multimodal data collection on battery stress, temperature, electricity, deformation, and thermal runaway related parameters; Step S2: preprocess the collected multimodal data and transfer the preprocessed data into the digital twin space; Step S3: Build a twin model of the battery in the twin space and perform physical modeling on the incoming data to monitor the current battery status; Step S4: deduce the battery's health status through a multimodal large model and predict the battery's future health status; Step S5: Installing gas sensors on the outside of the underground robot and inside and outside the explosion-proof compartment to monitor the gas concentration when the underground robot is working; Step S6: When the predicted health status value of the battery and the gas concentration are about to exceed the safety threshold, an early warning prompt is issued in the twin space through the early warning system; Step S7: After the early warning occurs, the safety of the underground robot working underground is ensured by controlling the operation of various safety elements.

[0008] Preferably, in step S1, an ultrasonic sensor, a voltage and current detection module, a method based on projection laser scanning, and a gas sensor are used to monitor and collect data from the downhole robot power unit, including: Stress and temperature data acquisition: Ultrasonic sensors are used to characterize the changing characteristics of the complex stress field inside the battery, based on ultrasonic acoustic elasticity and internal stress monitoring in the battery pack. The ultrasonic sensor consists of an ultrasonic longitudinal wave sensor and an ultrasonic surface wave sensor, which are symmetrically installed on both sides of the battery to obtain the internal stress state of the battery. Two ultrasonic surface wave sensors are installed on the upper surface of the battery to obtain the surface tension state of the battery pack. Electrical parameter acquisition: The voltage and current detection module is used to collect the internal voltage and current of the battery. The internal resistance of the battery is calculated based on the collected data, and the battery capacity is indirectly determined by combining it with the battery temperature data. Deformation data acquisition: Using a projection laser scanning method, a laser projector and a CCD camera are mounted on the inner wall of the explosion-proof compartment above the battery. The optical axis of the CCD camera must be perpendicular to the reference plane. When the battery is deformed due to complex working conditions, the CCD camera senses the deformation of the battery by collecting the projected fringes on the battery surface. Thermal runaway monitoring: Gas sensors are used to predict thermal runaway of batteries. Gas sensors are installed on the inner wall of the explosion-proof compartment to monitor the thermal runaway of the battery. concentration to predict early battery thermal runaway.

[0009] Preferably, in step S2, the collected multimodal data is preprocessed as follows: Step S21: decompose the signal into components of different frequencies by wavelet transform to remove noise, and then align different modal data according to timestamps; Step S22: Identify and eliminate outliers caused by measurement errors, and use a multi-source data fusion method to fill in the data loss caused by the complex communication environment underground; Step S23: Use the minimum-maximum normalization method to normalize the voltage, current, temperature and Normalize the gas concentration related data. Use the Z-score normalization method to normalize the stress and strain data with an approximately normal distribution, convert the data to a unified scale, and store the normalized data in a unified format. Step S24: extract key features from the processed data to obtain voltage change rate, current change rate, temperature gradient, stress-strain ratio and The gas concentration change rate is calculated and the associated modal features are fused into the state space modulus.

[0010] Preferably, in step S3, a multi-physics field coupling modeling method is used to comprehensively and intuitively present the power unit parameters of the downhole robot, including: Construct a stress-strain coupling model to monitor the deformation caused by battery stress; A thermoacoustic coupling-based model was constructed to monitor the internal temperature of the battery by simulating the changes in ultrasonic signal delay, attenuation, and dispersion caused by temperature. Construct an equivalent circuit model to predict the battery voltage and current. Calculate the battery's internal resistance using the collected voltage and current. Combined with the collected temperature data, construct a semi-empirical decay model to estimate the battery's capacity. Construct a temperature-deformation coupling model to monitor the relationship between the internal temperature and deformation of the battery to monitor the deformation of the battery; Build a thermal-electric coupling model to monitor the temperature distribution of the battery to determine the current and voltage status inside the battery; Construct an electric-mechanical coupling model to monitor the internal current and voltage of the battery in real time to prevent stress concentration caused by abnormal voltage and current; Constructing a thermal-gas coupling model to ensure that thermal runaway is avoided in the early temperature rise stage of the battery, thereby preventing Gas production.

[0011] Preferably, in step S4, the future health status of the battery is predicted by the method of co-evolution of the multimodal large model and the physical model, and the steps are as follows: Step S41: Extract features from each coupling model, and extract the rate of change, mean, variance, maximum, minimum and The rate of increase and trend of concentration; Step S42: Map the extracted features to the same semantic space for alignment, transform the features of different modalities through a fully connected layer, and optimize and adjust them through a loss function; Step S43: Use the series method to connect voltage, current, temperature, stress, strain and The aligned features of the concentrations are fused into a single joint representation, forming a joint feature vector; Step S44: After the features are successfully concatenated, a linear transformation layer is used to reduce the dimension and create a unified fused feature vector; Step S45: Select the ReLU activation function to construct a regression network and train it, input the fused feature vector into the regression network composed of the cross-modal attention network and the diffusion model, and output the predicted values ​​of each battery state parameter; Step S46: During the prediction process, the multimodal large model calls the physical coupling model to perform real-time reasoning calculations, uses the output of the physical coupling model as a constraint, and continuously optimizes its own prediction results.

[0012] Preferably, in step S5, a layered monitoring architecture is used to monitor the gas content inside and outside the downhole robot; a gas sensor is installed inside the robot, inside the explosion-proof compartment, and above the outside of the robot, and a concentration real-time mapping model is used to monitor the gas concentration in real time.

[0013] Preferably, in step S6, a multi-parameter comprehensive early warning method is used to provide intelligent early warning for the power unit of the underground robot, identify potential safety risks in advance, and provide sufficient time to take measures to reduce losses caused by accidents, including the following steps: Step S61, constructing a hybrid neural network: using a time convolutional network to process the time series data of voltage, current, and stress; converting the temperature data into a 3D point cloud format, and using a graph neural network to process the 3D temperature point cloud data; using a graph isomorphism network to process the strain data of the battery; using a graph neural network to process Gas concentration and spatial distribution data of gas concentration; Step S62: Concatenate the output of the physical coupling model, the output of the multimodal large model, and the real-time monitoring data, and concatenate the output of the real-time mapping model and the features of the real-time monitoring data to form two comprehensive feature vectors, which are then processed through a fully connected layer. Step S63: annotate the historical data to mark three different levels of status: normal state, abnormal state, and severe abnormal state, and train the hybrid neural network to optimize the model parameters; Step S64: Classify the warning results into different risk levels according to the risk indicators output by the hybrid neural network model, and generate corresponding system prompts according to the risk levels; Step S65: deploy the trained hybrid neural network model, taking into account the battery temperature, stress, current, voltage, Gas concentration and methane concentration: when one or more of these parameters show abnormal change trends or are about to exceed the safety threshold, the system will issue a corresponding warning.

[0014] Preferably, in step S7, when the underground robot generates an early warning, the emergency handling module dynamically adjusts the execution unit of the physical entity operation state to ensure the safety of the underground robot power unit, specifically including: A fire extinguishing device is installed inside the robot's explosion-proof compartment. When a thermal runaway warning occurs, the fire extinguishing device is controlled in the twin space to prevent the battery from burning or even exploding. When a deformation warning occurs, the emergency response module can reduce the load or control the internal pressure increase in real time, and cut off the working line when the set threshold is exceeded; When a stress warning occurs, the emergency response module adjusts the battery module output power to avoid aggravation of stress; During charging, when the voltage and current have an early warning, the emergency response module dynamically adjusts the charge and discharge current. When the voltage and current have an early warning during operation, the workload power is reduced. When the situation is difficult to control, the current is cut off in time. When the battery capacity is low, the emergency response module controls and reduces the battery discharge rate, limits the execution of high-power tasks, and automatically switches to low-power mode. When a temperature warning occurs, the emergency response module activates the intrinsically safe thermoelectric cooling system to efficiently absorb the heat generated by the battery and transfer it to the surrounding environment, thereby quickly reducing the battery temperature; When the gas concentration outside the robot exceeds the set threshold, the emergency response module immediately stops the robot's movement; When the gas concentration inside the robot exceeds the set threshold, the emergency response module cuts off the power supply to all electrical equipment; When the gas concentration inside the robot's explosion-proof compartment exceeds the set threshold, the emergency response module triggers the power-off locking mechanism of the explosion-proof compartment, cutting off the power supply to all electrical equipment in the compartment.

[0015] Preferably, a fire extinguishing device is installed inside the explosion-proof compartment of the robot, ensuring that the fire extinguishing port of the fire extinguishing device is aligned with the battery, and high-concentration liquid nitrogen is used as the fire extinguishing medium in the fire extinguishing device.

[0016] The present invention also provides a digital twin monitoring system for an underground robot power unit, which is composed of a battery, a multi-sensor module, a battery digital twin model, a data processing module, a physical modeling module, a multimodal large model, a gas sensor, an early warning module, and an emergency response module; Battery: A power element used to power the underground robot. It is the power unit of the underground robot and is located inside the explosion-proof compartment of the underground robot. Multi-sensor module: used to monitor the internal physical state of the battery and collect data; Battery digital twin model: used to map the battery in the physical space in real time in the twin space; Data processing module: used to process the data collected by the sensor module; Physical modeling module: used to perform physical modeling on the processed data; Multimodal large model: used to predict the future health status of the battery; Gas sensor: used to monitor the gas concentration inside and outside the robot and inside the explosion-proof compartment; Early warning module: used to analyze the battery status, detect potential risks in advance, and issue timely alarms; Emergency response module: used for rapid response and effective intervention measures, minimizing the occurrence of accidents and losses by controlling the operation of various safety components.

[0017] Therefore, the present invention proposes a digital twin monitoring method and system for the power unit of an underground robot, which has the following beneficial effects: (1) The data collected by multi-sensor fusion can provide richer information and support subsequent more accurate fault diagnosis and early warning functions. Ultrasonic acoustic elastic non-destructive monitoring technology can monitor the physical state inside the battery core, which is difficult to obtain with conventional sensors and physical observation methods.

[0018] (2) The wavelet transform method is used for denoising, which can retain low-frequency signals such as voltage trends. When processing current signals, background noise can be removed while retaining the peak and mutation of current. When processing battery temperature data in a high-noise environment, the threshold can be dynamically adjusted through the adaptive threshold method to better remove noise.

[0019] (3) By adopting a coupled physical field modeling approach, the interaction between multiple physical processes such as electricity, heat, and force is considered simultaneously, which more realistically reflects the dynamic behavior of the system under actual working conditions. This not only significantly improves the modeling accuracy, but also reveals the potential failure mechanism of the battery.

[0020] (4) A layered monitoring architecture is used for gas monitoring, which can timely detect gas problems inside the robot. The compartment layer monitors the concentration changes in the explosion-proof compartment, and the external layer monitors the external environmental conditions, thereby discovering hidden dangers in advance.

[0021] (5) By adopting a collaborative approach between a multimodal large model and a physical model, the output of the physical coupling model can be used as a constraint to continuously optimize its own prediction results, thereby achieving high-precision prediction and analysis of complex systems and improving the accuracy and reliability of the model.

[0022] (6) Using a multi-parameter comprehensive warning method to warn the battery can avoid the one-sidedness caused by a single physical field analysis, improve the accuracy of the warning, and capture potential abnormal conditions and fault signs in the system in advance, thereby achieving early warning.

[0023] (7) By using the emergency disposal module to carry out emergency treatment of the battery, the emergency plan can be quickly activated to ensure that effective measures are taken in the shortest time and to minimize the losses of the underground robot and the mine.

[0024] (8) Installing a fire extinguishing device inside the underground robot can help to quickly suppress combustion or explosion when the battery experiences thermal runaway, prevent the spread of fire and harmful gases, and thus greatly improve the robot's inherent safety in closed, high-risk environments.

[0025] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a digital twin monitoring method for a downhole robot power unit according to the present invention; Figure 2 This is a structural diagram of a digital twin monitoring system for a downhole robot power unit according to the present invention; Figure 3 It is a structural diagram of the interior of the explosion-proof compartment in the present invention; Figure 4 is a flow chart of multimodal data processing in the present invention; Figure 5 This is a flow chart of battery health status prediction in the present invention; Figure 6 This is an intelligent early warning flow chart of the underground robot power unit in the present invention.

[0027] Reference numerals 1. Gas sensor; 2. Fire extinguishing device; 3. Explosion-proof compartment; 4. Ultrasonic longitudinal wave sensor; 5. Ultrasonic surface wave sensor; 6. Laser projection device; 7. Gas sensor; 8. CCD camera; 9. Intrinsically safe thermoelectric cooling system; 10. Battery; 11. Voltage and current monitoring module. DETAILED DESCRIPTION

[0028] To make the technical solutions, advantages, and objectives of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below. The described embodiments are part of the embodiments of the present invention, not all of them. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0030] Example 1 like Figure 1 FIG. 1 is a flow chart of a digital twin monitoring method for a downhole robot power unit according to the present invention. The specific steps are as follows: S1. Perform multimodal data collection on battery stress, temperature, electrical, deformation, and thermal runaway parameters. The specific operations are as follows: The ultrasonic surface receiving sensor, the voltage and current detection module 11, the projection laser scanning method and the gas sensor 7 are used to monitor and collect data on the power unit of the underground robot, including: Stress and temperature data acquisition: A battery pack internal stress monitoring method based on ultrasonic acoustic elasticity is used to characterize the changing characteristics of the complex stress field inside the battery. The ultrasonic sensor consists of an ultrasonic longitudinal wave sensor 4 and an ultrasonic surface wave sensor 5. The ultrasonic longitudinal wave sensors 4 are symmetrically mounted on both sides of the battery 10 to obtain the internal stress state of the battery 10. Two ultrasonic surface wave sensors 5 are mounted on the upper surface of the battery 10 to obtain the surface tension state of the battery pack. Electrical parameter collection: The voltage and current detection module 11 is used to collect the internal voltage and current of the battery 10 to ensure that the battery 10 does not suffer from overcharging, over-discharging, or even short circuit problems. The internal resistance of the battery 10 can be calculated based on the collected data, and the capacity of the battery 10 can be indirectly determined by combining the battery 10 temperature data. Deformation data acquisition: A method based on projection laser scanning is used. The laser projection device 6 and the CCD camera 8 are respectively installed on the inner wall of the explosion-proof compartment above the battery 10. The optical axis of the CCD camera 8 must be perpendicular to the reference plane. When the battery 10 is deformed due to complex working conditions, the CCD camera 8 senses the deformation of the battery 10 by collecting the projection stripes of the deformed battery surface.

[0031] Thermal runaway monitoring: The gas sensor 7 is used to predict the thermal runaway of the battery 10. The gas sensor 7 is installed on the inner wall of the explosion-proof compartment to monitor the thermal runaway of the battery 10. concentration to predict early battery thermal runaway.

[0032] S2, such as Figure 4 As shown in the figure, the collected multimodal data is preprocessed and the preprocessed data is transferred to the digital twin space. The steps are as follows: S21, decompose the signal into components of different frequencies through wavelet transform to remove noise, and then align the different modal data according to the timestamp; S22, identify and eliminate outliers caused by measurement errors, and use multi-source data fusion methods to fill in the data loss caused by the complex communication environment underground; S23, using the minimum-maximum normalization method to normalize voltage, current, temperature and Normalize the gas concentration related data. Use the Z-score normalization method to normalize the stress and strain data with an approximately normal distribution, convert the data to a unified scale, and store the normalized data in a unified format. S24, extract key features from the processed data to obtain voltage change rate, current change rate, temperature gradient, stress-strain ratio and The gas concentration change rate is calculated and the associated modal features are fused into the state space modulus.

[0033] S3. Build a twin model of the battery in the twin space and perform physical modeling on the incoming data to monitor the current battery status; The multi-physics coupling modeling method is used to comprehensively and intuitively present the parameters of the downhole robot's power unit to cope with the impact of complex and changing conditions underground, including: Construct a stress-strain coupling model to monitor the deformation of the battery 10 caused by stress to prevent deformation of the battery cell and cracking of the casing; A model based on thermal-acoustic coupling is constructed to monitor the internal temperature of the battery 10 by simulating the changes in ultrasonic signal delay, attenuation, and dispersion caused by temperature; An equivalent circuit model is constructed to predict the voltage and current of the battery 10 to ensure that the battery does not overcharge, overdischarge, or short-circuit. The internal resistance of the battery 10 is calculated based on the collected voltage and current. The capacity of the battery 10 is estimated by constructing a semi-empirical decay model based on the collected temperature data to optimize the management of the battery 10. Constructing a temperature-deformation coupling model to monitor the relationship between the internal temperature and deformation of the battery 10 to monitor the deformation of the battery 10; Construct a thermal-electric coupling model to monitor the temperature distribution of the battery 10 to determine the current and voltage status inside the battery 10; Construct an electric-mechanical coupling model to monitor the internal current and voltage of the battery 10 in real time to prevent stress concentration caused by abnormal voltage and current; Construct a thermal-gas coupling model to monitor the thermal runaway caused by the temperature of the early battery 10 , ensuring that there is enough time to take appropriate measures to prevent thermal runaway or even explosion of the underground robot power unit.

[0034] S4, such as Figure 5 As shown in the figure, the health status of the battery is deduced and the future health status of the battery is predicted by coordinating the multimodal large model with the physical model. The steps are as follows: S41. Extract features from each coupling model, respectively extract the rate of change, mean, variance, maximum, minimum and The rising rate and changing trend of the concentration are used for subsequent alignment processing; S42: Map the extracted features to the same semantic space for alignment. Transform the features of different modalities through the fully connected layer so that their dimensions and scales are correctly matched to ensure compatibility with the next fusion step. Optimize and adjust them through the loss function. S43, use the series method to connect voltage, current, temperature, stress, strain and The aligned features of the concentrations are fused into a single joint representation, forming a joint feature vector; S44. After the features are successfully concatenated, a linear transformation layer is used to reduce the dimension and create a unified feature vector, so that the multimodal large model can learn the best representation of the fused features.

[0035] S45. Select the ReLU activation function to construct a regression network and train it, input the fused feature vector into the regression network composed of the cross-modal attention network and the diffusion model, and output the predicted values ​​of each battery state parameter; S46. During the prediction process, the multimodal large model calls the physical coupling model to perform real-time reasoning calculations, uses the output of the physical coupling model as a constraint, and continuously optimizes its own prediction results.

[0036] S5. Install a gas sensor on the outside of the underground robot and inside and outside the explosion-proof compartment to monitor the gas concentration when the underground robot is working. Specifically: A layered monitoring architecture is used to monitor the gas content inside and outside the underground robot; a gas sensor is installed inside the robot, inside the explosion-proof compartment 3, and above the outside of the robot to monitor the gas concentration. The external gas sensor ensures that the robot will not operate in an area where the gas concentration exceeds the safe value. The gas sensor inside the robot is used to ensure the sealing of the robot's internal space. The gas sensor 1 inside the explosion-proof compartment is used to ensure that the gas concentration will not explode due to abnormal power supply from the battery 10 or sparks generated during operation. A real-time concentration mapping model is used to monitor the gas concentration in real time.

[0037] S6, such as Figure 6 As shown in the figure, when the predicted health status of the battery and the gas concentration are about to exceed the safety threshold, the early warning system will issue an early warning prompt in the twin space. The specific steps are as follows: S61. Build a hybrid neural network: Use a time convolutional network to process time series data of voltage, current, and stress; convert temperature data into a 3D point cloud format and use a graph neural network to process the 3D temperature point cloud data; use a graph isomorphism network to process battery strain data; use a graph neural network to process Gas concentration and spatial distribution data of gas concentration; S62: splicing the output of the physical coupling model, the output of the multimodal large model, and the real-time monitoring data, splicing the output of the real-time mapping model and the features of the real-time monitoring data to form two comprehensive feature vectors, and processing the comprehensive feature vectors through a fully connected layer; S63, labeling historical data to mark three different levels of status: normal state, abnormal state, and severe abnormal state, and training the hybrid neural network to optimize model parameters; S64. Classify the warning results into different risk levels based on the risk indicators output by the hybrid neural network model, and generate corresponding system prompts based on the risk levels; S65, deploy the trained hybrid neural network model, taking into account the battery temperature, stress, current, voltage, Gas concentration and methane concentration: when one or more of these parameters show abnormal change trends or are about to exceed the safety threshold, the system will issue a corresponding warning.

[0038] S7. When the underground robot issues an early warning, the emergency response module dynamically adjusts the execution unit of the physical entity's operating state to ensure the safety of the underground robot's power unit, specifically including: A fire extinguishing device 2 is installed inside the explosion-proof compartment 3 of the robot. When a thermal runaway warning occurs, the fire extinguishing device 2 is controlled in the twin space to prevent the battery from entering the combustion or even explosion stage. The fire extinguishing device 2 uses high-concentration liquid nitrogen as a fire extinguishing medium. When a thermal runaway warning occurs, the low temperature of the high-concentration liquid nitrogen can quickly reduce the surface temperature of the battery 10, blocking the thermal runaway chain, and the evaporation of nitrogen can form a protective layer on the surface of the battery 10, thereby isolating oxygen and inhibiting the combustion reaction. Through the dual effects of rapid cooling and oxygen isolation and asphyxiation, the battery 10 is prevented from early thermal runaway and prevented from entering the combustion or even explosion stage.

[0039] When a deformation warning occurs, the emergency response module can reduce the load or control the internal pressure increase in real time, and cut off the working line when the set threshold is exceeded; When a stress warning occurs, the emergency response module adjusts the battery module output power to avoid aggravation of stress; During charging, when the voltage and current have an early warning, the emergency response module dynamically adjusts the charge and discharge current. When the voltage and current have an early warning during operation, the workload power is reduced. When the situation is difficult to control, the current is cut off in time. When the battery 10 capacity is under a warning, the emergency handling module controls and reduces the discharge rate of the battery 10, limits the execution of high-power tasks, and automatically switches to a low-power mode; When a temperature warning occurs, the emergency response module activates the intrinsically safe thermoelectric cooling system 9, which efficiently absorbs the heat generated by the battery 10 and transfers it to the surrounding environment, thereby quickly reducing the temperature of the battery 10; When the gas concentration outside the robot exceeds the set threshold, the emergency response module immediately stops the robot's movement; When the gas concentration inside the robot exceeds the set threshold, the emergency response module cuts off the power supply to all electrical equipment; When the gas concentration inside the explosion-proof compartment 3 of the robot exceeds the set threshold, the emergency response module triggers the power-off locking mechanism of the explosion-proof compartment 3, cutting off the power supply of all electrical equipment in the explosion-proof compartment 3 to prevent electric sparks from causing gas explosions.

[0040] Example 2 like Figure 2 As shown, the present invention also provides a digital twin monitoring system for a downhole robot power unit, which is composed of a battery, a multi-sensor module, a battery digital twin model, a data processing module, a physical modeling module, a multimodal large model, a gas sensor 1, an early warning module, and an emergency response module; Battery: a power element used to power the underground robot, serving as the underground robot power unit, located inside the underground robot explosion-proof compartment 3; The internal layout of the underground robot explosion-proof compartment 3 is as follows Figure 3As shown, it consists of a gas sensor 1, a fire extinguishing device 2, an ultrasonic longitudinal wave sensor 4, an ultrasonic surface wave sensor 5, a laser projection device 6, a gas sensor 7, a CCD camera 8, an intrinsically safe thermoelectric cooling system 9, a battery 10 and a voltage and current monitoring module 11.

[0041] Multi-sensor module: used to monitor the internal physical state of the battery 10 and collect data thereof; Battery digital twin model: used to map the battery 10 in the physical space in real time in the twin space; Data processing module: used to process the data collected by the sensor module; Physical modeling module: used to perform physical modeling on the processed data; Multimodal large model: used to predict the future health status of the battery 10; Gas sensor 1: used to monitor the gas concentration inside and outside the robot and inside the explosion-proof compartment 3; Early warning module: used to analyze the status of the battery 10, detect potential risks in advance, and issue an alarm in time; Emergency response module: used for rapid response and effective intervention measures, minimizing the occurrence of accidents and losses by controlling the operation of various safety components.

[0042] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0043] Therefore, this paper provides a digital twin monitoring method and system for the power unit of an underground robot. This system uses multiple sensors to collect battery data, constructs a multi-physics field coupling model, and integrates multimodal large-scale model analysis technology to achieve efficient and accurate monitoring and trend prediction of the power unit status. Furthermore, gas sensors are used to ensure a safe operating environment, and combined with early warning and emergency response modules, the system ensures the stable operation of the underground robot under complex working conditions.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A digital twin monitoring method for an underground robot power unit, characterized in that: Here are the steps: Step S1, performing multimodal data collection on battery stress, temperature, electricity, deformation, and thermal runaway related parameters; Step S2: preprocess the collected multimodal data and transfer the preprocessed data into the digital twin space; Step S3: Build a twin model of the battery in the twin space and perform physical modeling on the incoming data to monitor the current battery status; Step S4: deduce the battery's health status through a multimodal large model and predict the battery's future health status; Step S5: Installing gas sensors on the outside of the underground robot and inside and outside the explosion-proof compartment to monitor the gas concentration when the underground robot is working; Step S6: When the predicted health status value of the battery and the gas concentration are about to exceed the safety threshold, an early warning prompt is issued in the twin space through the early warning system; Step S7: After the early warning occurs, the safety of the underground robot working underground is ensured by controlling the operation of various safety elements.

2. A digital twin monitoring method for a downhole robot power unit according to claim 1, characterized in that: In step S1, ultrasonic sensors, voltage and current detection modules, a method based on projection laser scanning, and gas sensors are used to monitor and collect data from the downhole robot power unit, including: Stress and temperature data acquisition: Ultrasonic sensors are used to monitor stress within the battery pack based on ultrasonic acoustic elasticity to characterize the changing characteristics of the complex stress field inside the battery. Electrical parameter acquisition: The voltage and current detection module is used to collect the internal voltage and current of the battery. The internal resistance of the battery is calculated based on the collected data, and the battery capacity is indirectly determined by combining it with the battery temperature data. Deformation data acquisition: A projection laser scanning method is used to collect projection stripes of battery surface deformation to sense battery deformation; Thermal runaway monitoring: Use gas sensors to predict and monitor the thermal runaway of batteries.

3. A digital twin monitoring method for a downhole robot power unit according to claim 1, characterized in that: In step S2, the collected multimodal data is preprocessed as follows: Step S21: decompose the signal into components of different frequencies by wavelet transform to remove noise, and then align different modal data according to timestamps; Step S22: Identify and eliminate outliers caused by measurement errors, and use a multi-source data fusion method to fill in the data loss caused by the complex communication environment underground; Step S23: Use the minimum-maximum normalization method to normalize the voltage, current, temperature and Normalize the gas concentration related data. Use the Z-score normalization method to normalize the stress and strain data with an approximately normal distribution, convert the data to a unified scale, and store the normalized data in a unified format. Step S24: extract key features from the processed data to obtain voltage change rate, current change rate, temperature gradient, stress-strain ratio and The gas concentration change rate is calculated and the associated modal features are fused into the state space modulus.

4. A digital twin monitoring method for a downhole robot power unit according to claim 1, characterized in that: In step S3, a multi-physics field coupling modeling method is used to comprehensively and intuitively present the power unit parameters of the downhole robot, including: Construct a stress-strain coupling model to monitor the deformation caused by battery stress; A thermoacoustic coupling-based model was constructed to monitor the internal temperature of the battery by simulating the changes in ultrasonic signal delay, attenuation, and dispersion caused by temperature. Construct an equivalent circuit model to predict the battery voltage and current. Calculate the battery's internal resistance using the collected voltage and current. Combined with the collected temperature data, construct a semi-empirical decay model to estimate the battery's capacity. Construct a temperature-deformation coupling model to monitor the relationship between the internal temperature and deformation of the battery to monitor the deformation of the battery; Build a thermal-electric coupling model to monitor the temperature distribution of the battery to determine the current and voltage status inside the battery; Construct an electric-mechanical coupling model to monitor the internal current and voltage of the battery in real time to prevent stress concentration caused by abnormal voltage and current; Constructing a thermal-gas coupling model to ensure that thermal runaway is avoided in the early temperature rise stage of the battery, thereby preventing Gas production.

5. The method for monitoring a downhole robot power unit digital twin according to claim 1, characterized in that: In step S4, the future health status of the battery is predicted by the collaborative method of the multimodal large model and the physical model. The steps are as follows: Step S41: Extract features from each coupling model, and extract the rate of change, mean, variance, maximum, minimum and The rate of increase and trend of concentration; Step S42: Map the extracted features to the same semantic space for alignment, transform the features of different modalities through a fully connected layer, and optimize and adjust them through a loss function; Step S43: Use the series method to connect voltage, current, temperature, stress, strain and The aligned features of the concentrations are fused into a single joint representation, forming a joint feature vector; Step S44: After the features are successfully concatenated, a linear transformation layer is used to reduce the dimension and create a unified fused feature vector; Step S45: Select the ReLU activation function to construct a regression network and train it, input the fused feature vector into the regression network composed of the cross-modal attention network and the diffusion model, and output the predicted values ​​of each battery state parameter; Step S46: During the prediction process, the multimodal large model calls the physical coupling model to perform real-time reasoning calculations, uses the output of the physical coupling model as a constraint, and continuously optimizes its own prediction results.

6. A digital twin monitoring method for a downhole robot power unit according to claim 1, characterized in that: In step S5, a layered monitoring architecture is used to monitor the gas content inside and outside the downhole robot; a gas sensor is installed inside the robot, inside the explosion-proof compartment, and above the robot, and a concentration real-time mapping model is used to monitor the gas concentration in real time.

7. The method for monitoring a downhole robot power unit digital twin according to claim 1, characterized in that: In step S6, a multi-parameter comprehensive early warning method is used to provide intelligent early warning for the power unit of the underground robot, identify potential safety risks in advance, and take measures to reduce losses caused by accidents, including the following steps: Step S61, constructing a hybrid neural network: using a time convolutional network to process the time series data of voltage, current, and stress; converting the temperature data into a 3D point cloud format, and using a graph neural network to process the 3D temperature point cloud data; using a graph isomorphism network to process the strain data of the battery; using a graph neural network to process Gas concentration and spatial distribution data of gas concentration; Step S62: Concatenate the output of the physical coupling model, the output of the multimodal large model, and the real-time monitoring data, and concatenate the output of the real-time mapping model and the features of the real-time monitoring data to form two comprehensive feature vectors, which are then processed through a fully connected layer. Step S63: annotate the historical data to mark three different levels of status: normal state, abnormal state, and severe abnormal state, and train the hybrid neural network to optimize the model parameters; Step S64: Classify the warning results into different risk levels according to the risk indicators output by the hybrid neural network model, and generate corresponding system prompts according to the risk levels; Step S65: deploy the trained hybrid neural network model, taking into account the battery temperature, stress, current, voltage, Gas concentration and methane concentration: when one or more of these parameters show abnormal change trends or are about to exceed the safety threshold, the system will issue a corresponding warning.

8. The method for monitoring a downhole robot power unit digital twin according to claim 1, characterized in that: In step S7, when the underground robot issues an early warning, the emergency response module dynamically adjusts the execution unit of the physical entity's operating state to ensure the safety of the underground robot's power unit, specifically including: A fire extinguishing device is installed inside the robot's explosion-proof compartment. When a thermal runaway warning occurs, the fire extinguishing device is controlled in the twin space to prevent the battery from burning or even exploding. When a deformation warning occurs, the emergency response module can reduce the load or control the internal pressure increase in real time, and cut off the working line when the set threshold is exceeded; When a stress warning occurs, the emergency response module adjusts the battery module output power to avoid aggravation of stress; During charging, when the voltage and current have an early warning, the emergency response module dynamically adjusts the charge and discharge current. When the voltage and current have an early warning during operation, the workload power is reduced. When the situation is difficult to control, the current is cut off in time. When the battery capacity is low, the emergency response module controls and reduces the battery discharge rate, limits the execution of high-power tasks, and automatically switches to low-power mode. When a temperature warning occurs, the emergency response module activates the intrinsically safe thermoelectric cooling system to efficiently absorb the heat generated by the battery and transfer it to the surrounding environment, thereby quickly reducing the battery temperature; When the gas concentration outside the robot exceeds the set threshold, the emergency response module immediately stops the robot's movement; When the gas concentration inside the robot exceeds the set threshold, the emergency response module cuts off the power supply to all electrical equipment; When the gas concentration inside the robot's explosion-proof compartment exceeds the set threshold, the emergency response module triggers the power-off locking mechanism of the explosion-proof compartment, cutting off the power supply to all electrical equipment in the compartment.

9. A digital twin monitoring method for a downhole robot power unit according to claim 8, characterized in that: A fire extinguishing device is installed inside the explosion-proof compartment of the robot, ensuring that the fire extinguishing port of the fire extinguishing device is aimed at the battery, and high-concentration liquid nitrogen is used as the fire extinguishing medium in the fire extinguishing device.

10. A digital twin monitoring system for an underground robot power unit, characterized by: It consists of a battery, a multi-sensor module, a battery digital twin model, a data processing module, a physical modeling module, a multimodal large model, a gas sensor, an early warning module, and an emergency response module. Battery: A power element used to power the underground robot. It is the power unit of the underground robot and is located inside the explosion-proof compartment of the underground robot. Multi-sensor module: used to monitor the internal physical state of the battery and collect data; Battery digital twin model: used to map the battery in the physical space in real time in the twin space; Data processing module: used to process the data collected by the sensor module; Physical modeling module: used to perform physical modeling on the processed data; Multimodal large model: used to predict the future health status of the battery; Gas sensor: used to monitor the gas concentration inside and outside the robot and inside the explosion-proof compartment; Early warning module: used to analyze the battery status, detect potential risks in advance, and issue timely alarms; Emergency response module: used for rapid response and effective intervention measures, minimizing the occurrence of accidents and losses by controlling the operation of various safety components.

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