Intelligent switching method and device of dual power drive system of drilling coring robot

Abstract

The present invention provides a dual-power drive system intelligent switching method and device for a drilling coring robot, which relates to the technical field of system intelligent switching, including: connecting a dual-power drive system, wherein the first drive system is a motor direct drive system, and the second drive system is a hydraulic drive system; collecting data on the drilling environment to obtain drilling environment data; obtaining multiple exposure factors, which are environmental factors that affect the transmission torque of the drilling coring robot; obtaining multiple corresponding prior probabilities, predicting the variation of the transmission torque, and obtaining a first variation probability; when it is greater than a preset variation probability, obtaining a first switching instruction; after receiving the first switching instruction, switching from the first drive system to the second drive system. The present invention solves the technical problem that when a traditional drilling coring robot has excessive torque during drilling, the drill rod is prone to jamming or insufficient power, resulting in poor work adaptability and efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of system intelligent switching, and in particular to a method and device for intelligent switching of a dual-power drive system of a drilling and coring robot. Background Art

[0002] The main function of the drilling and coring robot is to conduct lunar drilling, including drilling on the lunar surface and subsurface, searching for water resources on the moon, and analyzing rock and soil samples on the lunar surface and subsurface to conduct geological composition research, solar system evolution research, and scientific experimental platform construction. In this process, the robot's stability, efficiency, and adaptability are crucial to improving the effectiveness of exploration work.

[0003] Traditional coring robots use corers to perform in-situ coring at great depths. However, although the coring robot is capable of performing coring operations at great depths on the moon, it uses a single type of drive system and is difficult to adapt to different working environments and workloads. When excessive torque occurs in complex situations, traditional methods cannot achieve intelligent switching of the drive system, and drill rod jamming or insufficient power may occur, resulting in poor stability, adaptability and efficiency of the robot. Therefore, different types of drive systems are needed to provide driving power to the drill rod under different torques, thereby improving the working stability, adaptability and efficiency of the coring robot. Summary of the invention

[0004] This application provides an intelligent switching method for the dual-power drive system of a drilling and coring robot, aiming to solve the technical problem that traditional drilling and coring robots are prone to drill rod jamming or insufficient power when excessive torque occurs during the drilling process, resulting in poor work adaptability and efficiency.

[0005] In view of the above problems, the present application provides a method and device for intelligently switching the dual-power drive system of a drilling and coring robot.

[0006] The first aspect disclosed in the present application provides an intelligent switching method for a dual-power drive system of a drilling and coring robot, the method comprising: connecting a dual-power drive system, the dual-power drive system being applied to the drilling and coring robot, the dual-power drive system comprising a first drive system and a second drive system, wherein the first drive system is a motor direct drive system, and the second drive system is a hydraulic drive system; collecting data on the drilling environment according to the drilling and coring robot to obtain drilling environment data; obtaining a plurality of exposure factors according to the drilling environment data, wherein the plurality of exposure factors are environmental factors that affect the transmission torque of the drilling and coring robot; obtaining a plurality of prior probabilities corresponding to the plurality of exposure factors, performing variation prediction on the transmission torque of the drilling and coring robot based on the plurality of prior probabilities to obtain a first variation probability; obtaining a first switching instruction when the first variation probability is greater than a preset variation probability; after receiving the first switching instruction, the dual-power drive system controls the drilling and coring robot to switch from the first drive system to the second drive system.

[0007] The second aspect disclosed in the present application provides an intelligent switching device for a dual-power drive system of a drilling and coring robot, wherein the device is used for an intelligent switching method for a dual-power drive system of the above-mentioned drilling and coring robot, and the device comprises: a drive system connection module, wherein the drive system connection module is used to connect a dual-power drive system, wherein the dual-power drive system is applied to the drilling and coring robot, and the dual-power drive system comprises a first drive system and a second drive system, wherein the first drive system is a motor direct drive system, and the second drive system is a hydraulic drive system; a data acquisition module, wherein the data acquisition module is used to collect data on the drilling environment according to the drilling and coring robot to obtain drilling environment data; an exposure factor acquisition module, wherein the exposure factor acquisition module The module is used to obtain multiple exposure factors according to the drilling environment data, wherein the multiple exposure factors are environmental factors that affect the transmission torque of the drilling and coring robot; a variation prediction module, the variation prediction module is used to obtain multiple prior probabilities corresponding to the multiple exposure factors, and perform variation prediction on the transmission torque of the drilling and coring robot based on the multiple prior probabilities to obtain a first variation probability; a switching instruction acquisition module, the switching instruction acquisition module is used to obtain a first switching instruction when the first variation probability is greater than a preset variation probability; a system switching module, the system switching module is used to control the drilling and coring robot to switch from the first drive system to the second drive system after the dual-power drive system receives the first switching instruction.

[0008] The third aspect disclosed in the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any step of the first aspect disclosed in the present application is implemented.

[0009] According to a fourth aspect disclosed in the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any step of the first aspect disclosed in the present application is implemented.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] By connecting the dual-power drive system and obtaining multiple exposure factors according to the drilling environment data, the key factors affecting the transmission torque are identified, and the intelligent perception of the drilling environment is realized; based on the obtained environmental data and multiple prior probabilities, the transmission torque is predicted by using an intelligent algorithm, and the first variation probability is calculated. By comparing it with the preset variation probability, it is intelligently judged whether the current working environment needs to switch the drive system; when the first variation probability is greater than the preset variation probability, a first switching instruction is generated to trigger the automatic switching of the drive system. This intelligent control mechanism can ensure that the robot can make appropriate responses in time when facing different work requirements, thereby improving the adaptability and flexibility of the robot. In summary, the intelligent switching method of the dual-power drive system of the drilling and coring robot realizes the intelligent switching of the drive system through intelligent perception, intelligent judgment, intelligent control and other means, thereby effectively improving the adaptability, efficiency and stability of the robot in complex working environments.

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic flow chart of an intelligent switching method for a dual-power drive system of a drilling and coring robot provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of an intelligent switching device for a dual-power drive system of a drilling and coring robot provided in an embodiment of the present application;

[0015] Figure 3 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application.

[0016] Explanation of the reference numerals: driving system connection module 10 , data acquisition module 20 , exposure factor acquisition module 30 , variation prediction module 40 , switching instruction acquisition module 50 , system switching module 60 . DETAILED DESCRIPTION

[0017] The embodiment of the present application solves the technical problem that the traditional coring drilling robot is prone to drill rod jamming or insufficient power when excessive torque occurs during the drilling process, resulting in poor work adaptability and efficiency by providing an intelligent switching method for the dual-power drive system of the coring drilling robot.

[0018] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] like Figure 1 As shown, the embodiment of the present application provides an intelligent switching method of a dual-power drive system of a drilling coring robot, the method comprising:

[0020] Connecting a dual-power drive system, the dual-power drive system is applied to a drilling coring robot, the dual-power drive system includes a first drive system and a second drive system, wherein the first drive system is a motor direct drive system, and the second drive system is a hydraulic drive system;

[0021] The core drilling robot is a robot used for lunar drilling. The dual power drive system is a combination of two different types of power drive systems used by the core drilling robot, which can provide flexibility and performance optimization to adapt to different operating requirements and working conditions.

[0022] Among them, the first drive system is the motor direct drive system in the dual-power drive system. The motor direct drive system directly transmits the rotational motion of the motor to the robot's actuator, and can directly convert the rotation of the motor into the movement of the robot. It is composed of a motor, a transmission device and a controller; the second drive system is the hydraulic drive system in the dual-power drive system. The hydraulic drive system uses hydraulic transmission to provide the power and motion control required by the robot, including components such as hydraulic pumps, hydraulic cylinders and control valves.

[0023] When faced with application scenarios that require rapid response to acceleration needs or bear heavy loads, the first drive system is switched to the second drive system; when faced with application scenarios that require continuous stability or poor mutation of operations, the second drive system is switched to the first drive system. Through the combination of these two different types of drive systems, the drilling and coring robot can flexibly select the appropriate power source under different working conditions to achieve efficient drilling operations.

[0024] Collecting data on the drilling environment according to the drilling and coring robot to obtain drilling environment data;

[0025] The drilling and coring robot needs to be equipped with various sensors to collect data on the drilling environment, such as depth sensors for measuring the drilling depth; temperature sensors for measuring the temperature of the drilling bit and the environment; pressure sensors for monitoring the rock pressure around the borehole; and vibration sensors for detecting vibrations during the drilling process. During the drilling process, the robot collects drilling environment data in real time through the above sensors, and integrates the collected environmental data to obtain a set of drilling environment data.

[0026] Acquire a plurality of exposure factors according to the drilling environment data, wherein the plurality of exposure factors are environmental factors that affect the transmission torque of the drilling and coring robot;

[0027] The collected drilling environment data is analyzed, and the environmental factors related to the drilling and coring robot transmission torque are identified according to its characteristics. These factors include but are not limited to formation hardness, borehole diameter and depth, ambient temperature and humidity, etc. For example, changes in ambient temperature and humidity will affect the robot's working state and lubrication effect, and thus affect the transmission torque. The multiple environmental factors obtained by the analysis are used as the multiple exposure factors, thereby providing accurate input data for the subsequent transmission torque variation prediction.

[0028] Acquire multiple prior probabilities corresponding to the multiple exposure factors, and perform variation prediction on the transmission torque of the drilling and coring robot based on the multiple prior probabilities to acquire a first variation probability;

[0029] The impact effects between multiple exposure factors and transmission torque are analyzed to obtain multiple corresponding prior probabilities. Prior probability refers to the estimate of the probability of an event occurring without observing any other information. Here, it represents the prior probability of each exposure factor for transmission torque variation, which is similar to the probability set in advance.

[0030] Based on multiple prior probabilities, the transmission torque variation prediction can be carried out by using the weighted fusion method, taking the prior probabilities of various exposure factors as weights to comprehensively predict the variation of the transmission torque. Weighted fusion can ensure that factors with greater influence are given higher weights, thereby improving the accuracy of the prediction. After weighted fusion calculation, the first variation probability is obtained, which represents the overall probability estimate of the transmission torque variation after combining the influence of various exposure factors. The specific prior probability calculation and variation prediction process will be detailed in the subsequent steps.

[0031] When the first mutation probability is greater than a preset mutation probability, obtaining a first switching instruction;

[0032] The preset mutation probability is a pre-set threshold value, which indicates the tolerance for the transmission torque variation probability and is set according to system performance requirements, safety requirements, etc. The calculated first variation probability is compared with the preset variation probability. If the first variation probability is greater than the preset variation probability, it means that there are certain factors in the environment that will cause a large degree of variation in the transmission torque, and a system switch is required. The first switching instruction is obtained. The first switching instruction includes a command to switch to another drive system, such as switching from a motor direct drive system to a hydraulic drive system, to cope with the transmission torque variation.

[0033] After receiving the first switching instruction, the dual-power drive system controls the drilling and coring robot to switch from the first drive system to the second drive system.

[0034] After receiving the first switching instruction, the dual-power drive system prepares to switch the drive system, including pausing the current working state, stopping the operation of the motor or hydraulic system, etc. After being prepared, the switching operation begins, switching the drilling and coring robot from the first drive system to the second drive system. For the switching of the motor direct drive system and the hydraulic drive system, the switching includes switching valves, adjusting the pressure of the hydraulic system, and adjusting the start and stop control of the motor. After the switching is completed, the system restores the working state of the drilling and coring robot and starts to continue the drilling operation.

[0035] Furthermore, the method for predicting the variation of the transmission torque of the drilling and coring robot based on the multiple prior probabilities includes:

[0036] Based on the multiple prior probabilities, the multiple exposure factors are calculated using the total probability formula to output a first variation probability, wherein the first variation probability is a fusion probability of different effects of each exposure factor on the occurrence of transmission torque variation, and the calculation formula of the first variation probability is as follows:

[0037] ;

[0038] in, is the first mutation probability, For the The prior probability corresponding to the exposure factor represents the The effect of each exposure factor on the transmission torque variation. is the number of exposure factors, Based on The probability of transmission torque variation under the prior probability of the exposure factors.

[0039] Specifically, the calculation formula of the first mutation probability is as follows:

[0040] ;

[0041] in, is the first variation probability, which indicates the variation probability of the transmission torque, that is, the value to be predicted. This probability indicates how likely the transmission torque is to vary under given conditions; For the The prior probability corresponding to an exposure factor indicates the influence of this factor on the variation of transmission torque without considering other factors; is the number of exposure factors, indicating how many factors affect the variation of transmission torque; is the probability of transmission torque variation under the condition of the i-th exposure factor. In other words, it represents the possibility of transmission torque variation under the influence of a known factor.

[0042] Through this formula, the prior probability of each exposure factor and their influence on the transmission torque variation can be comprehensively considered to calculate the overall variation probability of the transmission torque, which helps to predict the change of the transmission torque during the operation of the drilling and coring robot and guide subsequent operations.

[0043] Furthermore, the first drive system and the second drive system are also connected to a gear transmission system, and the method further includes:

[0044] Simulating the gear transmission system and outputting gear transmission loss data in a first switching mode, wherein the first switching mode is a mode in which the first drive system switches to the second drive system;

[0045] Perform simulation according to the gear transmission system, and output gear transmission loss data in a second switching mode, wherein the second switching mode is a mode in which the second drive system switches to the first drive system;

[0046] generating a switching constraint condition according to the gear transmission loss data in the first switching mode and the gear transmission loss data in the second switching mode;

[0047] The switching of the dual-power drive system is constrained based on the switching constraint condition.

[0048] The gear transmission system is a mechanical transmission system that transmits power and torque through the meshing of gears, thereby realizing motion transmission between different components. In the dual power drive system of the drilling and coring robot, the first drive system and the second drive system are connected to the gear transmission system, which means that they transmit power and control the movement of the robot through the gear transmission system.

[0049] A model of the gear transmission system is established, which includes all gears, transmission ratios, bearings and other related components. Mechanical simulation software is used to simulate the first switching mode, that is, the mode in which the first drive system switches to the second drive system. The first switching mode may cause changes in parameters such as component configuration, transmission path, and rotation speed in the gear transmission system.

[0050] During the simulation, various losses such as mechanical friction and gear meshing loss in the transmission process are obtained by analyzing each gear and transmission path. These losses are usually expressed in the form of energy and can be directly obtained through computer simulation software. The simulation results are output as gear transmission loss data in the first switching mode, which includes information such as the loss value of each gear and transmission path, the total loss value, etc.

[0051] Similar to the first switching mode, the second switching mode, i.e., the mode in which the second drive system switches to the first drive system, is simulated to obtain the gear transmission loss data in the second switching mode. This step is the same as the aforementioned first switching mode and will not be repeated here for the sake of brevity.

[0052] When the switching is too frequent, resulting in excessive loss, it is necessary to reduce the total number of switching times, set a preset loss index, and optimize the first loss index in the first switching mode and the second loss index in the second switching mode with the preset loss index as the target, and output the switching constraint condition with the optimized number of switching as the constraint target to ensure that the system can remain stable during switching and avoid the adverse effects caused by frequent switching. The specific optimization process will be detailed in the subsequent steps.

[0053] The obtained switching constraints are applied to the switching of the dual-power drive system, that is, the switching constraints are considered in the system control logic and used as constraints for the switching decision. The switching trigger mechanism is determined based on the switching constraints to ensure that the system will only perform the switching operation under the premise of meeting the constraints. For example, the switching is only performed when the switching trigger conditions are met and the switching interval time requirements are met. In this way, the switching of the dual-power drive system can be effectively constrained to ensure that the system can remain stable during the switching process and follow the pre-set switching constraints to meet the working requirements of the drilling and coring robot.

[0054] Further, according to the gear transmission loss data in the first switching mode and the gear transmission loss data in the second switching mode, generating a switching constraint condition includes:

[0055] Set preset loss indicators;

[0056] generating a first loss index under the first switching mode and a second loss index under the second switching mode according to the gear transmission loss data under the first switching mode and the gear transmission loss data under the second switching mode;

[0057] Taking the preset loss index as the target, taking the first loss index and the second loss index as input variables, performing a number optimization, and outputting the number of switching obtained by the optimization;

[0058] The switching number obtained by optimization is used as the constraint target, and the switching constraint condition is output.

[0059] The purpose of setting the preset loss index is to determine the loss range allowed in the switching mode. On the one hand, considering the design requirements and application scenarios of the drilling and coring robot, the preset loss index should ensure the performance indicators of the system in different working modes. On the other hand, considering the safety of robot operation, the preset loss index should ensure that the robot can still operate safely within the loss range and will not cause damage to the equipment. According to the system performance requirements and safety aspects, based on engineering experience and previous experimental data, the preset loss index is determined. This index is a specific value that represents the maximum allowable loss value.

[0060] For the gear transmission loss data in the first switching mode, the loss values ​​of each component are added, including mechanical friction loss, gear meshing loss and other losses, to obtain the total loss value, which is set as the first loss index in the first switching mode. This index reflects the amount of loss generated when the system switches in the first switching mode. When switching frequently, the amount of loss increases and the loss is too large. At this time, the total number of switching needs to be reduced.

[0061] For the loss in the second switching mode, the total loss value is also calculated and set as the second loss index, which reflects the amount of loss generated when the system switches in the second switching mode.

[0062] The preset loss index is used as the optimization target, which represents the upper limit of the system loss in the switching mode. The first loss index and the second loss index are used as the input variables for optimization, which represent the actual loss of the system in the switching mode. An optimization algorithm is selected, including gradient descent method, genetic algorithm, particle swarm optimization algorithm, etc. The selected optimization algorithm is used to perform a number search to find the optimal number of switching operations that minimizes the target. This number represents the optimal number of switching operations that should be performed under the preset loss index, so as to effectively control the frequency of switching operations and ensure the energy loss of the system in the switching mode.

[0063] According to the optimized switching times, combined with the system's working cycle, switching time and other factors, the switching constraints are formulated, including setting the switching time interval, etc. The switching constraints are combined with the switching trigger conditions to ensure that the system can perform switching operations according to actual needs while meeting the preset loss indicators and switching frequency requirements.

[0064] Further, according to the gear transmission loss data in the first switching mode and the gear transmission loss data in the second switching mode, generating a switching constraint condition, the method further includes:

[0065] Recording the first switching frequency of the first switching mode;

[0066] Recording the second switching frequency of the second switching mode;

[0067] Calculate according to the first switching frequency and the first loss index, and the second loss index and the second switching frequency, and output a loss index sum;

[0068] Obtaining a difference between the loss index and the preset loss index, optimizing the preset mutation probability according to the difference, and outputting the preset mutation probability after optimization;

[0069] The preset mutation probability after optimization is used as the constraint target, and the switching constraint condition is output.

[0070] During system operation, the switching operations in the first switching mode and the second switching mode are monitored in real time, and the counter value of each switching is recorded. According to the recorded switching operation data, the switching frequencies of the first switching mode and the second switching mode, i.e., the first switching frequency and the second switching frequency, are calculated by counting the number of switching times within a certain period of time.

[0071] Using the first switching frequency and the first loss index, the first loss index sum is calculated, which can be obtained by multiplying the first switching frequency by the first loss index, reflecting the total loss of the system in the first switching mode. Similarly, using the second switching frequency and the second loss index, the second loss index sum is calculated, which represents the total loss of the system in the second switching mode. The first loss index and the second loss index are summed to obtain the loss index sum, which reflects the total loss of the system in the two switching modes.

[0072] Subtract the loss index from the preset loss index to get the difference between them. This difference represents the deviation between the actual loss and the expected loss. The preset mutation probability is optimized using the difference. If the difference is positive, it means that the actual loss exceeds the expected loss, and the preset mutation probability needs to be increased to reduce the loss; if the difference is negative, the preset mutation probability is reduced to improve the performance of the system.

[0073] According to the size and direction of the difference, the value of the preset mutation probability is adjusted, and an optimization algorithm, such as gradient descent or genetic algorithm, is used to find the optimal preset mutation probability that minimizes the loss. The optimized and adjusted preset mutation probability is used as the output result for subsequent system control.

[0074] The preset mutation probability after optimization is used as the constraint target, and the switching trigger condition is set in the system. For example, the switching operation will be triggered only when the first mutation probability is greater than the preset mutation probability after optimization. The switching trigger condition is used as part of the switching constraint condition to ensure that the switching operation is performed within the preset mutation probability range after optimization.

[0075] Furthermore, obtaining a plurality of prior probabilities corresponding to the plurality of exposure factors comprises:

[0076] Analyzing the multiple exposure factors using Mendelian randomization, and establishing an influence effect model between the multiple exposure factors and transmission torque;

[0077] Outputting a plurality of confidence intervals corresponding to the plurality of exposure factors according to the impact effect model;

[0078] The multiple confidence intervals are analyzed and output as the multiple prior probabilities.

[0079] Mendelian randomization is a method that uses measured genetic variation to confirm the causal effect of exposure on the outcome. Here, the exposure factor is analyzed by randomization to ensure the credibility of the experimental results. First, the goal of the effect model to be established is clarified, that is, the degree of influence of different environmental factors on the transmission torque. The experiment is designed using Mendelian randomization to reduce other factors that may affect the results, including the setting of the experimental group and the control group, as well as the control and change of the exposure factor. On the basis of the experimental design, data collection is carried out to collect the transmission torque data and the corresponding exposure factor data under each experimental condition. To analyze the collected data, statistical methods such as regression analysis and variance analysis can be used for data fitting to obtain the relationship between the exposure factor and the transmission torque. By analyzing the obtained data, an effect model between the exposure factor and the transmission torque is established.

[0080] Parameter estimation is performed on the established impact effect model. Statistical methods, such as the least squares method, are used to estimate the parameters in the model to obtain the impact effect coefficient of each exposure factor. The confidence interval of each parameter is calculated based on the results of parameter estimation and the standard error of the model. For example, the confidence interval is calculated using t distribution or normal distribution. The common confidence level is 95%.

[0081] The calculated confidence intervals are associated with the corresponding exposure factors to form multiple confidence intervals corresponding to multiple exposure factors. These confidence intervals represent the uncertainty range of the impact effects of the corresponding exposure factors. A wider confidence interval indicates a higher uncertainty in the estimate.

[0082] Each confidence interval is analyzed, and the characteristics and importance of the confidence interval corresponding to each exposure factor are determined according to the confidence level and interval range, and the characteristics of the confidence interval are converted into prior probabilities. For example, a wider confidence interval corresponds to a higher prior probability, indicating that the uncertainty of the degree of impact of the exposure factor is greater; while a narrower confidence interval corresponds to a lower prior probability, indicating that the uncertainty of the degree of impact of the exposure factor is smaller. Based on the analysis results of the confidence interval, the prior probability corresponding to each exposure factor is determined. These prior probabilities reflect the initial estimate of the impact of each exposure factor on the transmission torque in the absence of other information, which is similar to the probability obtained by setting in advance.

[0083] Furthermore, the method further comprises:

[0084] When the current driving system of the coring drilling robot is the second driving system, a stable prediction is performed on the operating state of the coring drilling robot according to a plurality of prior probabilities corresponding to the plurality of exposure factors to obtain a first stable probability;

[0085] When the first stable probability is greater than a preset stable probability, obtaining a second switching instruction;

[0086] After receiving the second switching instruction, the dual-power drive system controls the drilling and coring robot to switch from the second drive system to the first drive system.

[0087] When the current driving system of the drilling and coring robot is the second driving system, the current drilling environment data is obtained, including the measured values ​​of various exposure factors, including environmental factors such as temperature, pressure, and humidity. Based on the prior probability and environmental data, a prediction model for the operating status is established. This model can be established using machine learning methods, such as neural networks, etc. The established prediction model is trained using historical data, and the model parameters are adjusted so that it can more accurately predict the operating status of the robot.

[0088] The trained model is used to predict the current environmental data to obtain the predicted operating state of the drilling and coring robot. According to the predicted operating state, the working stability of the robot is evaluated to obtain the stability probability of the drilling and coring robot. The calculated stability probability is used as the first stability probability. This probability reflects the possibility that the robot's operating state is stable when the current drive system is the second drive system.

[0089] The preset stability probability is a threshold value set in advance, which is used to judge whether the working state of the drilling and coring robot under the current drive system is sufficiently stable. This probability can be determined according to actual needs and historical experience. The first stability probability is compared with the preset stability probability. If the first stability probability is greater than the preset stability probability, it means that the current working state of the robot is relatively stable. In this case, the second switching instruction is obtained, which includes switching from the second drive system to the first drive system.

[0090] When the dual-power drive system receives the second switching instruction, it first parses the received second switching instruction to determine the required switching operation, that is, switching from the second drive system to the first drive system. Before performing the switching operation, it ensures that the drilling and coring robot is in a safe state and stops the ongoing task. When ready, it can perform the switching operation, including shutting down the second drive system and starting the first drive system.

[0091] In summary, the intelligent switching method of the dual-power drive system of the drilling and coring robot provided in the embodiment of the present application has the following technical effects:

[0092] 1. By connecting the dual power drive system and obtaining multiple exposure factors based on drilling environment data, the key factors affecting the transmission torque are identified, thus realizing intelligent perception of the drilling environment;

[0093] 2. Based on the obtained environmental data and multiple prior probabilities, the intelligent algorithm is used to predict the variation of the transmission torque, and the first variation probability is calculated. By comparing it with the preset variation probability, it is intelligently determined whether the current working environment requires the switching of the drive system;

[0094] 3. When the first mutation probability is greater than the preset mutation probability, a first switching instruction is generated to trigger the automatic switching of the drive system. This intelligent control mechanism can ensure that the robot can make appropriate responses in a timely manner when facing different work requirements, thereby improving the robot's adaptability and flexibility.

[0095] In summary, the intelligent switching method of the dual-power drive system of the drilling and coring robot realizes the intelligent switching of the drive system through intelligent perception, intelligent judgment, intelligent control and other means, thereby effectively improving the adaptability, efficiency and stability of the robot in complex working environments.

[0096] Based on the same inventive concept as the intelligent switching method of the dual-power drive system of the drilling and coring robot in the aforementioned embodiment, Figure 2 As shown, the present application provides an intelligent switching device for a dual-power drive system of a drilling and coring robot, the device comprising:

[0097] A drive system connection module 10, wherein the drive system connection module 10 is used to connect a dual-power drive system, wherein the dual-power drive system is applied to a drilling coring robot, wherein the dual-power drive system comprises a first drive system and a second drive system, wherein the first drive system is a motor direct drive system, and the second drive system is a hydraulic drive system;

[0098] A data acquisition module 20, wherein the data acquisition module 20 is used to collect data on the drilling environment according to the drilling and coring robot to obtain drilling environment data;

[0099] An exposure factor acquisition module 30, wherein the exposure factor acquisition module 30 is used to acquire a plurality of exposure factors according to the drilling environment data, wherein the plurality of exposure factors are environmental factors that affect the transmission torque of the drilling and coring robot;

[0100] A variation prediction module 40, the variation prediction module 40 is used to obtain a plurality of prior probabilities corresponding to the plurality of exposure factors, and perform variation prediction on the transmission torque of the drilling and coring robot based on the plurality of prior probabilities to obtain a first variation probability;

[0101] A switching instruction acquisition module 50, wherein the switching instruction acquisition module 50 is used to acquire a first switching instruction when the first mutation probability is greater than a preset mutation probability;

[0102] The system switching module 60 is used to control the drilling and coring robot to switch from the first drive system to the second drive system after the dual-power drive system receives the first switching instruction.

[0103] Furthermore, the device further includes a first mutation probability calculation module to perform the following operation steps:

[0104] Based on the multiple prior probabilities, the multiple exposure factors are calculated using the total probability formula to output a first variation probability, wherein the first variation probability is a fusion probability of different effects of each exposure factor on the occurrence of transmission torque variation, and the calculation formula of the first variation probability is as follows:

[0105] ;

[0106] in, is the first mutation probability, For the The prior probability corresponding to the exposure factor represents the The effect of each exposure factor on the transmission torque variation. is the number of exposure factors, Based on The probability of transmission torque variation under the prior probability of the exposure factors.

[0107] Furthermore, the device further comprises a switching constraint module to perform the following operation steps:

[0108] Simulating the gear transmission system and outputting gear transmission loss data in a first switching mode, wherein the first switching mode is a mode in which the first drive system switches to the second drive system;

[0109] Perform simulation according to the gear transmission system, and output gear transmission loss data in a second switching mode, wherein the second switching mode is a mode in which the second drive system switches to the first drive system;

[0110] generating a switching constraint condition according to the gear transmission loss data in the first switching mode and the gear transmission loss data in the second switching mode;

[0111] The switching of the dual-power drive system is constrained based on the switching constraint condition.

[0112] Furthermore, the device further comprises a switching constraint condition output module to perform the following operation steps:

[0113] Set preset loss indicators;

[0114] generating a first loss index under the first switching mode and a second loss index under the second switching mode according to the gear transmission loss data under the first switching mode and the gear transmission loss data under the second switching mode;

[0115] Taking the preset loss index as the target, taking the first loss index and the second loss index as input variables, performing a number optimization, and outputting the number of switching obtained by the optimization;

[0116] The switching number obtained by optimization is used as the constraint target, and the switching constraint condition is output.

[0117] Furthermore, the device further comprises a constraint condition output module to perform the following operation steps:

[0118] Recording the first switching frequency of the first switching mode;

[0119] Recording the second switching frequency of the second switching mode;

[0120] Calculate according to the first switching frequency and the first loss index, and the second loss index and the second switching frequency, and output a loss index sum;

[0121] Obtaining a difference between the loss index and the preset loss index, optimizing the preset mutation probability according to the difference, and outputting the preset mutation probability after optimization;

[0122] The preset mutation probability after optimization is used as the constraint target, and the switching constraint condition is output.

[0123] Furthermore, the device further includes a priori probability generation module to perform the following operation steps:

[0124] Analyzing the multiple exposure factors using Mendelian randomization, and establishing an influence effect model between the multiple exposure factors and transmission torque;

[0125] Outputting a plurality of confidence intervals corresponding to the plurality of exposure factors according to the impact effect model;

[0126] The multiple confidence intervals are analyzed and output as the multiple prior probabilities.

[0127] Furthermore, the device further includes a system switching module to perform the following operation steps:

[0128] When the current driving system of the coring drilling robot is the second driving system, a stable prediction is performed on the operating state of the coring drilling robot according to a plurality of prior probabilities corresponding to the plurality of exposure factors to obtain a first stable probability;

[0129] When the first stable probability is greater than a preset stable probability, obtaining a second switching instruction;

[0130] After receiving the second switching instruction, the dual-power drive system controls the drilling and coring robot to switch from the second drive system to the first drive system.

[0131] Through the above-mentioned detailed description of the intelligent switching method of the dual power drive system of the drilling and coring robot in this specification, those skilled in the art can clearly know the intelligent switching device of the dual power drive system of the drilling and coring robot in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0132] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in FIG. Figure 3As shown. The computer device includes a processor, a memory and a network interface connected through a device bus, wherein the processor of the computer device is used to provide computing and control capabilities; the memory of the computer device includes a non-volatile storage medium and an internal memory, the non-volatile storage medium stores an operating device, a computer program and a database, and the internal memory provides an environment for the operation of the operating device and the computer program in the non-volatile storage medium; the network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement an intelligent switching method for a dual-power drive system of a drilling and coring robot.

[0133] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0134] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0135] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent switching method for a dual-power drive system of a drilling and coring robot, characterized in that: The method comprises: Connecting a dual-power drive system, the dual-power drive system is applied to a drilling coring robot, the dual-power drive system includes a first drive system and a second drive system, wherein the first drive system is a motor direct drive system, and the second drive system is a hydraulic drive system; Collecting data on the drilling environment according to the drilling and coring robot to obtain drilling environment data; Acquire a plurality of exposure factors according to the drilling environment data, wherein the plurality of exposure factors are environmental factors that affect the transmission torque of the drilling and coring robot; Acquire multiple prior probabilities corresponding to the multiple exposure factors, and perform variation prediction on the transmission torque of the drilling and coring robot based on the multiple prior probabilities to acquire a first variation probability; When the first mutation probability is greater than a preset mutation probability, obtaining a first switching instruction; After receiving the first switching instruction, the dual-power drive system controls the drilling and coring robot to switch from the first drive system to the second drive system; Based on the multiple prior probabilities, the transmission torque of the drilling and coring robot is predicted to vary, and the method includes: Based on the multiple prior probabilities, the multiple exposure factors are calculated using the total probability formula to output a first variation probability, wherein the first variation probability is a fusion probability of different effects of each exposure factor on the occurrence of transmission torque variation, and the calculation formula of the first variation probability is as follows: ; in, is the first mutation probability, For the The prior probability corresponding to the exposure factor represents the The effect of each exposure factor on the transmission torque variation. is the number of exposure factors, Based on The probability of transmission torque variation under the prior probability of the exposure factors; Acquiring multiple prior probabilities corresponding to the multiple exposure factors, the method includes: Analyzing the multiple exposure factors using Mendelian randomization, and establishing an influence effect model between the multiple exposure factors and transmission torque; Outputting a plurality of confidence intervals corresponding to the plurality of exposure factors according to the impact effect model; The multiple confidence intervals are analyzed and output as the multiple prior probabilities.

2. The method according to claim 1, characterized in that The first drive system and the second drive system are also connected to a gear transmission system, and the method further includes: Simulating the gear transmission system and outputting gear transmission loss data in a first switching mode, wherein the first switching mode is a mode in which the first drive system switches to the second drive system; Perform simulation according to the gear transmission system, and output gear transmission loss data in a second switching mode, wherein the second switching mode is a mode in which the second drive system switches to the first drive system; generating a switching constraint condition according to the gear transmission loss data in the first switching mode and the gear transmission loss data in the second switching mode; The switching of the dual-power drive system is constrained based on the switching constraint condition.

3. The method according to claim 2, characterized in that Generating a switching constraint condition according to the gear transmission loss data in the first switching mode and the gear transmission loss data in the second switching mode, including: Set preset loss indicators; generating a first loss index under the first switching mode and a second loss index under the second switching mode according to the gear transmission loss data under the first switching mode and the gear transmission loss data under the second switching mode; Taking the preset loss index as the target, taking the first loss index and the second loss index as input variables, performing a number optimization, and outputting the number of switching obtained by the optimization; The switching number obtained by optimization is used as the constraint target, and the switching constraint condition is output.

4. The method according to claim 3, characterized in that Generating a switching constraint condition according to the gear transmission loss data in the first switching mode and the gear transmission loss data in the second switching mode, the method further comprising: Recording the first switching frequency of the first switching mode; Recording the second switching frequency of the second switching mode; Calculate according to the first switching frequency and the first loss index, and the second loss index and the second switching frequency, and output a loss index sum; Obtaining a difference between the loss index and the preset loss index, optimizing the preset mutation probability according to the difference, and outputting the preset mutation probability after optimization; The preset mutation probability after optimization is used as the constraint target, and the switching constraint condition is output.

5. The method according to claim 1, characterized in that The method further comprises: When the current driving system of the coring drilling robot is the second driving system, a stable prediction is performed on the operating state of the coring drilling robot according to a plurality of prior probabilities corresponding to the plurality of exposure factors to obtain a first stable probability; When the first stable probability is greater than a preset stable probability, obtaining a second switching instruction; After receiving the second switching instruction, the dual-power drive system controls the drilling and coring robot to switch from the second drive system to the first drive system.

6. The dual-power drive system intelligent switching device of the drilling and coring robot is characterized in that: A method for intelligently switching a dual-power drive system of a drilling and coring robot according to any one of claims 1 to 5, the device comprising: A drive system connection module, wherein the drive system connection module is used to connect a dual-power drive system, wherein the dual-power drive system is applied to a drilling coring robot, wherein the dual-power drive system comprises a first drive system and a second drive system, wherein the first drive system is a motor direct drive system, and the second drive system is a hydraulic drive system; A data acquisition module, wherein the data acquisition module is used to collect data on the drilling environment according to the drilling and coring robot to obtain drilling environment data; An exposure factor acquisition module, the exposure factor acquisition module is used to acquire a plurality of exposure factors according to the drilling environment data, wherein the plurality of exposure factors are environmental factors that affect the transmission torque of the drilling and coring robot; A variation prediction module, the variation prediction module is used to obtain a plurality of prior probabilities corresponding to the plurality of exposure factors, and perform variation prediction on the transmission torque of the drilling and coring robot based on the plurality of prior probabilities to obtain a first variation probability; A switching instruction acquisition module, wherein the switching instruction acquisition module is used to acquire a first switching instruction when the first mutation probability is greater than a preset mutation probability; A system switching module, wherein the system switching module is used to control the drilling and coring robot to switch from the first drive system to the second drive system after the dual-power drive system receives the first switching instruction.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the intelligent switching method of the dual-power drive system of the drilling and coring robot described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent switching method of the dual-power drive system of the drilling and coring robot according to any one of claims 1 to 5 are implemented.

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