CN119177840B - Intelligent switching method and device of dual power drive system of drilling coring robot - Google Patents
Intelligent switching method and device of dual power drive system of drilling coring robot Download PDFInfo
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- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
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Abstract
本发明提供了钻探取芯机器人的双动力驱动系统智能切换方法及装置，涉及系统智能切换技术领域，包括：连接双动力驱动系统，第一驱动系统为电机直驱系统，第二驱动系统为液压驱动系统；对钻探环境进行数据采集，获取钻探环境数据；获取多个暴露因子，为影响钻探取芯机器人传动扭矩的环境因子；获取对应的多个先验概率，对传动扭矩进行变异预测，获取第一变异概率；当大于预设变异概率时，获取第一切换指令；接收第一切换指令后，由第一驱动系统切换至第二驱动系统。本发明解决了传统的钻探取芯机器人在钻探过程中出现过大扭矩时，易发生钻杆卡滞或动力不足的现象，导致工作的适应性和效率较差的技术问题。
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 the drilling process, the drill rod is prone to jamming or insufficient power, resulting in poor work adaptability and efficiency.
Description
Technical Field
The invention relates to the technical field of intelligent system switching, in particular to an intelligent switching method and device for a double-power driving system of a drilling coring robot.
Background
The drilling coring robot has the main functions of performing lunar drilling, including drilling the lunar surface and the lower surface layer, searching water resources on the moon, and realizing geological composition research, solar system evolution research, scientific experiment platform construction and the like based on rock and soil sample analysis of the lunar surface and the lower surface layer.
The traditional drilling coring robot realizes in-situ large-depth coring through the coring device, however, although the drilling coring robot can finish the large-depth coring operation on the moon, a single type of driving system is adopted, the drilling coring robot is difficult to adapt to different working environments and working loads, when excessive torque occurs in the complex situation easily, the intelligent switching of the driving system cannot be realized by the traditional method, the phenomenon of drill rod clamping stagnation or insufficient power easily occurs, the stability, the adaptability and the efficiency of the robot are poor, and therefore, different types of driving systems are needed to realize the driving power supply of drill rods under different torques, and the working stability, the adaptability and the efficiency of the drilling coring robot are improved.
Disclosure of Invention
The application provides an intelligent switching method of a double-power driving system of a drilling coring robot, which aims to solve the technical problems of poor working adaptability and efficiency caused by the phenomenon of easy drill rod clamping stagnation or insufficient power when the traditional drilling coring robot has excessive torque in the drilling process.
In view of the above problems, the present application provides a method and apparatus for intelligent switching of a dual power drive system of a drilling coring robot.
The application discloses a first aspect, an intelligent switching method of a double-power driving system of a drilling coring robot is provided, the intelligent switching method comprises the steps of connecting the double-power driving system, wherein the double-power driving system is applied to the drilling coring robot and comprises a first driving system and a second driving system, the first driving system is a motor direct driving system, the second driving system is a hydraulic driving system, data acquisition is conducted on a drilling environment according to the drilling coring robot, drilling environment data are obtained, a plurality of exposure factors are obtained according to the drilling environment data, the exposure factors are environmental factors which influence transmission torque of the drilling coring robot, a plurality of priori probabilities corresponding to the exposure factors are obtained, variation prediction is conducted on the transmission torque of the coring drilling robot based on the prior probabilities, a first variation probability is obtained, when the first variation probability is larger than the preset variation probability, the double-power driving system receives the first switching instruction, and then the drilling robot is controlled to be switched from the first driving system to the second driving system.
The application discloses a second aspect, an intelligent switching device of a double-power driving system of a drilling coring robot, which is used for the intelligent switching method of the double-power driving system of the drilling coring robot, and comprises a driving system connecting module, a variation prediction module, a first switching probability obtaining module and a first switching probability obtaining module, wherein the driving system connecting module is used for connecting the double-power driving system, the double-power driving system is applied to the drilling coring robot and comprises a first driving system and a second driving system, the first driving system is a motor direct driving system, the second driving system is a hydraulic driving system, the data collecting module is used for collecting data of a drilling environment according to the drilling coring robot, the exposure factor obtaining module is used for obtaining a plurality of exposure factors according to the drilling environment data, the variation prediction module is used for obtaining a plurality of probabilities corresponding to the plurality of exposure factors, when the first switching probability obtaining a first switching probability of the coring robot is used for obtaining a first switching probability of the drilling coring driving system, and the first switching probability obtaining a first switching probability is used for obtaining a first switching probability of the drilling system when the first switching probability is used for obtaining a first switching command of the drilling system.
In a third aspect of the present disclosure, there is provided a computer device comprising a memory storing a computer program and a processor implementing any of the steps of the first aspect of the present disclosure when the computer program is executed by the processor.
In a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs any of the steps of the first aspect of the present disclosure.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The dual-power driving system is connected, a plurality of exposure factors are obtained according to drilling environment data, key factors influencing transmission torque are identified, and intelligent sensing of the drilling environment is realized; based on the obtained environment data and a plurality of prior probabilities, the variation prediction is carried out on the transmission torque by using an intelligent algorithm, a first variation probability is calculated, whether the current working environment needs to be switched to a driving system or not is intelligently judged by comparing the first variation probability with a preset variation probability, when the first variation probability is larger than the preset variation probability, a first switching instruction is generated to trigger the automatic switching of the driving system, and the intelligent control mechanism can ensure that the robot can respond appropriately in time when facing different working requirements, so that the adaptability and the flexibility of the robot are improved. In summary, the intelligent switching method of the double-power driving system of the drilling coring robot realizes the intelligent switching of the driving system through means of intelligent sensing, intelligent judgment, intelligent control and the like, thereby effectively improving the adaptability, the efficiency and the stability of the robot in a complex working environment.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Fig. 1 is a schematic flow chart of an intelligent switching method of a dual-power driving system of a drilling coring robot according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an intelligent switching device of a dual-power driving system of a drilling coring robot according to an embodiment of the present application;
fig. 3 is an internal structure diagram of a computer device according to an embodiment of the present application.
Reference numerals illustrate the drive system connection module 10, the data acquisition module 20, the exposure factor acquisition module 30, the variance prediction module 40, the switching instruction acquisition module 50, and the system switching module 60.
Detailed Description
The embodiment of the application solves the technical problems of poor working adaptability and efficiency caused by the phenomenon of easy drill rod clamping stagnation or insufficient power when the traditional drilling coring robot has excessive torque in the drilling process by providing the intelligent switching method of the double-power driving system of the drilling coring robot.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
As shown in fig. 1, an embodiment of the present application provides a dual power drive system intelligent switching method of a drilling coring robot, the method comprising:
The system comprises a drilling coring robot, a dual-power driving system, a motor direct-drive system, a hydraulic driving system and a control system, wherein the dual-power driving system is connected with the dual-power driving system and applied to the drilling coring robot and comprises a first driving system and a second driving system;
The drill coring robot is a robot for performing lunar drilling. The dual power drive system is a combination of two different types of power drive systems employed by the drill coring robot, which may provide flexibility and performance optimization to accommodate different operating requirements and working conditions.
The first driving system is a motor direct-drive system in the double-power driving system, the motor direct-drive system directly transmits the rotary motion of a motor to an execution part of the robot, the motor direct-drive system can directly convert the rotation of the motor into the motion of the robot, the robot is composed of the motor, a transmission device and a controller, the second driving system is a hydraulic driving system in the double-power driving system, and the hydraulic driving system utilizes hydraulic transmission to provide power and motion control required by the robot and comprises components such as a hydraulic pump, a hydraulic cylinder, a control valve and the like.
The system is switched from the first driving system to the second driving system when the system is in the application scene of rapid response to acceleration requirements or bearing heavy load, and is switched from the second driving system to the first driving system when the system is in the application scene of continuous stability and poor mutation of the operation. By a combination of these two different types of drive systems, the drilling coring robot can flexibly select a suitable power source under different working conditions to achieve an efficient drilling operation.
Acquiring data of a drilling environment according to the drilling coring robot to acquire drilling environment data;
The drilling coring robot needs to be equipped with various sensors to collect data of the drilling environment, such as a depth sensor for measuring the depth of drilling, a temperature sensor for measuring the temperature of the drilling bit and the environment, a pressure sensor for monitoring rock pressure around the borehole, and a vibration sensor for detecting vibration conditions during drilling. The robot collects drilling environment data in real time through the various sensors in the drilling process, and integrates the collected environment data to obtain a drilling environment data set.
Acquiring a plurality of exposure factors according to the drilling environment data, wherein the plurality of exposure factors are environment factors influencing the transmission torque of the drilling coring robot;
The acquired drilling environment data is analyzed, and environmental factors related to the drilling coring robot are identified according to the characteristics of the transmission torque of the drilling coring robot, wherein the factors include, but are not limited to, formation hardness, drilling diameter and depth, environment temperature and humidity and the like, and for example, the change of the environment temperature and the humidity can influence the working state and the lubrication effect of the robot, and further influence the transmission torque. And taking the plurality of environmental factors obtained by analysis as the plurality of exposure factors, thereby providing accurate input data for subsequent transmission torque variation prediction.
Acquiring a plurality of prior probabilities corresponding to the plurality of exposure factors, and performing variation prediction on the transmission torque of the drilling coring robot based on the plurality of prior probabilities to acquire a first variation probability;
The effect of influence between the plurality of exposure factors and the transmission torque is analyzed to obtain a corresponding plurality of prior probabilities, which refers to an estimate of the occurrence probability of an event without any other information being observed, where it represents the prior probability of each exposure factor for transmission torque variation, similar to the probability set in advance.
The method can be used for comprehensively predicting the variation of the transmission torque by taking the prior probability of each exposure factor as a weight based on a plurality of prior probabilities, the weighted fusion can ensure that higher weight is given to factors with larger influence, so that the accuracy of prediction is improved, and the first variation probability is obtained through weighted fusion calculation and represents the overall probability estimation of the variation of the transmission torque after the influence of each exposure factor is combined. The specific prior probability calculation and mutation prediction processes are developed in detail in the subsequent steps.
When the first variation probability is larger than a preset variation probability, a first switching instruction is obtained;
A threshold value preset for the variation probability is preset, the tolerance of the variation probability of the transmission torque is expressed, and the transmission torque is set according to the aspects of system performance requirements, safety requirements and the like. Comparing the calculated first variation probability with a preset variation probability, if the first variation probability is larger than the preset variation probability, the fact that the variation degree of the transmission torque is larger due to certain factors in the environment is described, system switching is needed to be carried out, a first switching instruction is obtained, and the first switching instruction comprises a command for switching to another driving system, for example, a motor direct-drive system is switched to a hydraulic driving system, so that the situation of variation of the transmission torque is dealt with.
And after the double-power driving system receives the first switching instruction, the drilling coring robot is controlled to be switched from the first driving system to the second driving system.
After receiving the first switching instruction, the double-power driving system prepares to switch the driving system, including suspending the current working state, stopping the operation of the motor or the hydraulic system, and the like. And after the operation is ready, switching the drilling coring robot from the first driving system to the second driving system, and switching the motor direct driving system and the hydraulic driving system comprises operations of switching a valve, adjusting the pressure of the hydraulic system, adjusting the start-stop control of the motor and the like. After the switch is completed, the system resumes the operating state of the drilling coring robot and begins to continue the drilling operation.
Further, a variation prediction is performed on the transmission torque of the drilling coring robot based on the plurality of prior probabilities, the method comprising:
Based on the multiple prior probabilities, calculating the multiple exposure factors by using a full probability formula, and outputting a first variation probability, wherein the first variation probability is a fusion probability of each exposure factor to generate different influence effects on occurrence of transmission torque variation, and the calculation formula of the first variation probability is as follows:
;
Wherein, For the first probability of variation to be the first,Is the firstThe prior probability corresponding to each exposure factor represents the firstThe effects of exposure factors on the occurrence of transmission torque variations,In order to expose the number of factors,Is based on the firstProbability of drive torque variation under a priori probability of individual exposure factors.
Specifically, the calculation formula of the first variation probability is as follows:
;
Wherein, For the first probability of variation, the probability of variation of the transmission torque, i.e. the value to be predicted, is expressed, which probability indicates how likely the transmission torque is to be varied under the given conditions; Is the first The prior probability corresponding to the exposure factors shows the influence of the factors on the variation of the transmission torque under the condition that other factors are not considered; to expose the number of factors, it is indicated how many factors affect the variation in drive torque; given the ith exposure factor, the probability of transmission torque variation, in other words, it represents the probability of transmission torque variation under the influence of a factor known.
Through the formula, the prior probability of each exposure factor and the influence effect of each exposure factor on the variation of the transmission torque can be comprehensively considered, so that the overall variation probability of the transmission torque is calculated, the variation condition of the transmission torque can be predicted in the working process of the drilling coring robot, and further the subsequent operation is guided.
Further, the first drive system and the second drive system are also coupled to a gear train, the method further comprising:
According to the simulation of the gear transmission system, outputting gear transmission loss data in a first switching mode, wherein the first switching mode is a mode of switching the first driving system to the second driving system;
according to the simulation of the gear transmission system, outputting gear transmission loss data in a second switching mode, wherein the second switching mode is a mode that the second driving system is switched to the first driving 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;
And constraining the switching of the double-power driving system based on the switching constraint condition.
In a dual power drive system of a drilling coring robot, the first drive system and the second drive system are connected with the gear drive system, which means that they transmit power through the gear drive system and control the movement of the robot.
A model of the gear train is built, which includes all gears, gear ratios, bearings and other related components, and a mechanical simulation software is used to simulate a first switching mode, i.e. a mode in which the first drive system is switched to the second drive system, which may cause parameters such as component configuration, transmission path, rotational speed, etc. in the gear train to change.
In the simulation process, through analysis of each gear and transmission path, various losses such as mechanical friction, gear engagement loss and the like in the transmission process are obtained, and the losses are usually expressed in terms of energy and can be directly obtained through computer simulation software. The simulation results are output as gear loss data in the first shift mode, which includes information such as loss values, total loss values, etc. for the individual gears and transmission paths.
The step of performing simulation on the second switching mode, that is, the mode of switching the second driving system to the first driving system, to obtain the gear transmission loss data in the second switching mode is the same as that of the first switching mode, and is not repeated herein for brevity of description.
When the switching is too frequent, the loss is too large, the total switching times are required to be reduced, a preset loss index is set, the times of optimizing the first loss index in the first switching mode and the second loss index in the second switching mode are carried out by taking the preset loss index as a target, the optimized switching times are taken as constraint targets, and the switching constraint conditions are output, so that the system can be ensured to be kept stable when switching is carried out, and adverse effects caused by frequent switching are avoided. The specific optimization process is developed in detail in the subsequent steps.
The obtained switching constraint is applied in switching of the dual power drive system, i.e. the switching constraint is taken into account in the system control logic and taken as a constraint of the switching decision. The switching trigger mechanism is determined based on the switching constraint condition, so that the system can execute switching operation on the premise that the constraint condition is met, for example, switching is performed only when the switching trigger condition is met and the switching interval time requirement is met. Therefore, the switching of the double-power driving system can be effectively restrained, the system can be ensured to be kept stable in the switching process, and preset switching constraint conditions are followed, so that the working requirements of the drilling coring robot are met.
Further, 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 includes:
Setting a preset loss index;
Generating a first loss index in the first switching mode and a second loss index in the second switching mode according to the gear transmission loss data in the first switching mode and the gear transmission loss data in the second switching mode;
Performing frequency optimization by taking the preset loss index as a target and taking the first loss index and the second loss index as input variables, and outputting the optimized switching frequency;
And taking the optimized switching times as constraint targets, and outputting the switching constraint conditions.
The preset loss index is set to determine the allowable loss range in the switching mode, on one hand, the preset loss index is set to ensure the performance index of the system in different working modes in consideration of the design requirement and application scene of the drilling coring robot, and on the other hand, the preset loss index is set to ensure that the robot can still safely run in the loss range without damaging equipment in consideration of the operation safety of the robot. According to the system performance requirements and safety aspects, a preset loss index is determined based on engineering experience and previous experimental data, wherein the index is a specific numerical value and represents an allowable maximum loss value.
For the transmission loss data in the first switching mode, the loss values of all the components are added, including various losses such as mechanical friction loss, gear engagement loss and the like, so as to obtain a total loss value, and the total loss value is set as a first loss index in the first switching mode, wherein the index reflects the loss amount generated when the system is switched in the first switching mode. When switching frequently, the loss increases and the loss becomes excessive, and at this time, the total number of switching needs to be reduced.
For the loss in the second switching mode, the total loss value thereof is also calculated and set as a second loss index reflecting the amount of loss generated at the time of system switching in the second switching mode.
The preset loss index is used as an optimization target, and the target represents the upper limit of the system loss in the switching mode. The first loss index and the second loss index are used as optimized input variables, and the variables represent the actual loss condition of the system in the switching mode. And selecting an optimization algorithm, including a gradient descent method, a genetic algorithm, a particle swarm optimization algorithm and the like, optimizing the times by using the selected optimization algorithm, and finding the optimal switching times for minimizing the target, wherein the optimal switching times represent the optimal switching times which should be performed under a preset loss index, so that the frequency of switching operation is effectively controlled, and the energy loss of the system in a switching mode is ensured.
And according to the optimized switching times, combining factors such as the working period, the switching time and the like of the system, and formulating switching constraint conditions, including setting switching time intervals and the like. And combining the switching constraint condition with the switching trigger condition to ensure that the system can perform switching operation according to actual needs on the premise of meeting the requirements of a preset loss index and switching frequency.
Further, 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 includes:
recording a first switching frequency of the current first switching mode;
recording a second switching frequency of the current second switching mode;
Calculating according to the first switching frequency and the first loss index, and the second loss index and the second switching frequency, and outputting a loss index sum;
Obtaining a difference value between the loss index and the preset loss index, optimizing the preset variation probability according to the difference value, and outputting the optimized preset variation probability;
and taking the optimized preset variation probability as a constraint target, and outputting the switching constraint condition.
In the running process of the system, switching operation in the first switching mode and the second switching mode is monitored in real time, the counter value of each switching is recorded, and switching frequencies of the first switching mode and the second switching mode, namely the first switching frequency and the second switching frequency, are obtained through calculation by counting switching times in a certain time period according to recorded switching operation data.
The first loss index sum is calculated using the first switching frequency and the first loss index, which may 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. Likewise, a second loss index sum is calculated using the second switching frequency and the second loss index, which represents the total loss of the system in the second switching mode. The first loss indicator and the second loss indicator are summed to obtain a loss indicator sum, which reflects the total loss of the system in both switching modes.
The loss index sum is subtracted from the preset loss index to obtain a difference therebetween, which represents a deviation between the actual loss and the expected loss. Optimizing the preset variation probability by using the difference value, if the difference value is positive, indicating that the actual loss exceeds the expected loss, and adjusting the preset variation probability upwards to reduce the loss; if the difference is negative, the preset variation probability is adjusted down instead to improve the performance of the system.
According to the magnitude and direction of the difference value, the value of the preset variation probability is adjusted, an optimization algorithm, such as a gradient descent method or a genetic algorithm, is adopted to find the optimal preset variation probability for minimizing the loss, and the preset variation probability after optimization adjustment is used as an output result for subsequent system control.
And setting a switching trigger condition in the system by taking the optimized preset variation probability as a constraint target, for example, triggering switching operation when the first variation probability is larger than the optimized preset variation probability, taking the switching trigger condition as a part of the switching constraint condition, and ensuring that the switching operation is performed within the optimized preset variation probability range.
Further, the method for obtaining the prior probabilities corresponding to the exposure factors includes:
Analyzing the plurality of exposure factors by using Mendelian randomization, and establishing an effect model of influence between the plurality of exposure factors and transmission torque;
outputting a plurality of confidence intervals corresponding to the plurality of exposure factors according to the influence effect model;
and analyzing the confidence intervals and outputting the confidence intervals as the prior probabilities.
Mendelian randomization is a method of using measured genetic variation to confirm the causal impact of exposure on the results, where exposure factors are analyzed by randomization to ensure the credibility of the experimental results. Firstly, the target of an influence effect model to be established, namely, the influence degree of different environmental factors on transmission torque is clarified, mendelian randomized design experiment is used to reduce other factors which can influence results, including the setting of an experimental group and a control group, and the control and variation modes of exposure factors, data collection is carried out on the basis of experimental design, and transmission torque data and corresponding exposure factor data under each experimental condition are collected. And analyzing the collected data, performing data fitting by adopting a statistical method such as regression analysis, variance analysis and the like, obtaining the relation between the exposure factor and the transmission torque, and establishing an influence effect model between the exposure factor and the transmission torque by analyzing the obtained data.
And estimating parameters of the established influence effect model, estimating the parameters in the model by using a statistical method, such as a least square method, so as to obtain an influence effect coefficient of each exposure factor, and calculating a confidence interval of each parameter according to a result of parameter estimation and a standard error of the model, wherein the confidence interval is calculated by adopting t distribution or normal distribution, and the common confidence level is 95%.
And correlating the calculated confidence intervals with the corresponding exposure factors to form a plurality of confidence intervals corresponding to the exposure factors, wherein the confidence intervals represent the uncertainty range of the influence effect of the corresponding exposure factors, and the wider confidence intervals represent higher estimated uncertainty.
Analyzing each confidence interval, determining the characteristics and importance of the confidence interval corresponding to each exposure factor according to the confidence level and interval range, converting the characteristics of the confidence interval into prior probabilities, wherein the wider confidence interval has higher prior probability and shows larger uncertainty of the influence degree of the exposure factor, and the narrower confidence interval has lower prior probability and shows smaller uncertainty of the influence degree of the exposure factor. And determining the prior probability corresponding to each exposure factor according to the analysis result of the confidence interval, wherein the prior probabilities reflect the initial estimation of the influence of each exposure factor on the transmission torque without other information, and are similar to the probabilities obtained by the advanced setting.
Further, the method further comprises:
when the current driving system of the drilling coring robot is the second driving system, carrying out stable prediction on the working state of the drilling coring robot according to a plurality of prior probabilities corresponding to a plurality of exposure factors, and obtaining a first stable probability;
when the first stability probability is larger than a preset stability probability, a second switching instruction is acquired;
and after the double-power driving system receives the second switching instruction, controlling the drilling coring robot to be switched to the first driving system by the second driving system.
When the current drive system of the drilling coring robot is the second drive system, current drilling environment data is acquired, including measured values of various exposure factors, including environmental factors such as temperature, pressure, humidity, and the like. Based on the prior probability and the environmental data, a prediction model of the operation state is established, the model can be established by adopting a machine learning method, such as a neural network, and the like, the established prediction model is trained by using historical data, and the operation state of the robot can be accurately predicted by adjusting model parameters.
And predicting the current environmental data by using the trained model to obtain a predicted operation state of the drilling coring robot, evaluating the working stability of the robot according to the predicted operation state, obtaining the stability probability of the drilling coring robot, taking the calculated stability probability as a first stability probability, wherein the probability reflects the possibility of stable operation state of the robot when the current driving system is a second driving system.
The preset stability probability is a preset threshold value, and is used for judging whether the working state of the drilling coring robot under the current driving system is stable enough or not, and the probability can be determined according to actual requirements and historical experience. Comparing the first stability probability with a preset stability probability, and if the first stability probability is larger than the preset stability probability, indicating that the current working state of the robot is relatively stable, in this case, acquiring a second switching instruction, wherein the instruction comprises switching from the second driving system to the first driving system.
When the dual power drive system receives the second switching instruction, the received second switching instruction is first parsed to determine the required switching operation, i.e., switching from the second drive system to the first drive system, before the switching operation is performed, the drilling coring robot is ensured to be in a safe state and stop the ongoing task, and when ready, the switching operation can be performed, including switching off the second drive system and starting the first drive system.
In summary, the intelligent switching method of the dual-power driving system of the drilling coring robot provided by the embodiment of the application has the following technical effects:
1. The dual-power driving system is connected, a plurality of exposure factors are obtained according to drilling environment data, key factors influencing transmission torque are identified, and intelligent sensing of the drilling environment is realized;
2. Based on the obtained environmental data and a plurality of prior probabilities, carrying out variation prediction on the transmission torque by utilizing an intelligent algorithm, calculating a first variation probability, and intelligently judging whether the current working environment needs to switch a driving system or not by comparing the first variation probability with a preset variation probability;
3. When the first variation probability is larger than the preset variation probability, a first switching instruction is generated to trigger the driving system to automatically switch, and the intelligent control mechanism can ensure that the robot can timely respond to different working demands, so that the adaptability and the flexibility of the robot are improved.
In summary, the intelligent switching method of the double-power driving system of the drilling coring robot realizes the intelligent switching of the driving system through means of intelligent sensing, intelligent judgment, intelligent control and the like, thereby effectively improving the adaptability, the efficiency and the stability of the robot in a complex working environment.
Based on the same inventive concept as the dual power drive system intelligent switching method of the drilling coring robot in the previous embodiment, as shown in fig. 2, the present application provides a dual power drive system intelligent switching device of the drilling coring robot, the device comprising:
The driving system connecting module 10 is used for connecting a double-power driving system, the double-power driving system is applied to the drilling coring robot and comprises a first driving system and a second driving system, wherein the first driving system is a motor direct driving system, and the second driving system is a hydraulic driving system;
the data acquisition module 20 is used for acquiring data of a drilling environment according to the drilling coring robot, so as to acquire drilling environment data;
an exposure factor acquisition module 30, the exposure factor acquisition module 30 configured to acquire a plurality of exposure factors from the drilling environment data, wherein the plurality of exposure factors are environmental factors that affect a transmission torque of a drilling coring robot;
the variation prediction module 40 is configured to obtain a plurality of prior probabilities corresponding to the plurality of exposure factors, perform variation prediction on the transmission torque of the drilling coring robot based on the plurality of prior probabilities, and obtain a first variation probability;
The switching instruction obtaining module 50 is configured to obtain a first switching instruction when the first variation probability is greater than a preset variation probability;
The system switching module 60 is configured to control the drilling coring robot to switch from the first driving system to the second driving system after the dual-power driving system receives the first switching instruction.
Further, the apparatus further includes a first mutation probability calculation module to perform the following operation steps:
Based on the multiple prior probabilities, calculating the multiple exposure factors by using a full probability formula, and outputting a first variation probability, wherein the first variation probability is a fusion probability of each exposure factor to generate different influence effects on occurrence of transmission torque variation, and the calculation formula of the first variation probability is as follows:
;
Wherein, For the first probability of variation to be the first,Is the firstThe prior probability corresponding to each exposure factor represents the firstThe effects of exposure factors on the occurrence of transmission torque variations,In order to expose the number of factors,Is based on the firstProbability of drive torque variation under a priori probability of individual exposure factors.
Further, the apparatus also includes a handover constraint module to perform the following operation steps:
According to the simulation of the gear transmission system, outputting gear transmission loss data in a first switching mode, wherein the first switching mode is a mode of switching the first driving system to the second driving system;
according to the simulation of the gear transmission system, outputting gear transmission loss data in a second switching mode, wherein the second switching mode is a mode that the second driving system is switched to the first driving 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;
And constraining the switching of the double-power driving system based on the switching constraint condition.
Further, the device also comprises a switching constraint condition output module for executing the following operation steps:
Setting a preset loss index;
Generating a first loss index in the first switching mode and a second loss index in the second switching mode according to the gear transmission loss data in the first switching mode and the gear transmission loss data in the second switching mode;
Performing frequency optimization by taking the preset loss index as a target and taking the first loss index and the second loss index as input variables, and outputting the optimized switching frequency;
And taking the optimized switching times as constraint targets, and outputting the switching constraint conditions.
Further, the device also comprises a constraint condition output module for executing the following operation steps:
recording a first switching frequency of the current first switching mode;
recording a second switching frequency of the current second switching mode;
Calculating according to the first switching frequency and the first loss index, and the second loss index and the second switching frequency, and outputting a loss index sum;
Obtaining a difference value between the loss index and the preset loss index, optimizing the preset variation probability according to the difference value, and outputting the optimized preset variation probability;
and taking the optimized preset variation probability as a constraint target, and outputting the switching constraint condition.
Further, the apparatus further includes a priori probability generating module to perform the following operation steps:
Analyzing the plurality of exposure factors by using Mendelian randomization, and establishing an effect model of influence between the plurality of exposure factors and transmission torque;
outputting a plurality of confidence intervals corresponding to the plurality of exposure factors according to the influence effect model;
and analyzing the confidence intervals and outputting the confidence intervals as the prior probabilities.
Further, the device also comprises a system switching module for executing the following operation steps:
when the current driving system of the drilling coring robot is the second driving system, carrying out stable prediction on the working state of the drilling coring robot according to a plurality of prior probabilities corresponding to a plurality of exposure factors, and obtaining a first stable probability;
when the first stability probability is larger than a preset stability probability, a second switching instruction is acquired;
and after the double-power driving system receives the second switching instruction, controlling the drilling coring robot to be switched to the first driving system by the second driving system.
The foregoing detailed description of the intelligent switching method of the dual power driving system of the drilling and coring robot will clearly be known to those skilled in the art, and the intelligent switching device of the dual power driving system of the drilling and coring robot in this embodiment is relatively simple in description, and the relevant points refer to the description of the method section.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in FIG. 3. The computer equipment comprises a processor, a memory and a network interface which are connected through a device bus, wherein the processor of the computer equipment is used for providing computing and control capability, the memory of the computer equipment comprises a nonvolatile storage medium and an internal memory, the nonvolatile storage medium stores an operating device, a computer program and a database, the internal memory is used for providing an environment for the operation of the operating device and the computer program in the nonvolatile storage medium, and the network interface of the computer equipment is used for communicating with an external terminal through network connection. The computer program is executed by the processor to implement a dual power drive system intelligent switching method of the drilling coring robot.
It will be appreciated by those skilled in the art that the structure shown in FIG. 3 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
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| US11927965B2 (en) * | 2016-02-29 | 2024-03-12 | AI Incorporated | Obstacle recognition method for autonomous robots |
| CN112031739B (en) * | 2020-08-27 | 2021-08-24 | 江苏辰午节能科技股份有限公司 | Dual-motor driving system for petroleum drilling and control method thereof |
| CN214616344U (en) * | 2020-10-10 | 2021-11-05 | 李星宇 | Multifunctional drilling machine |
| CN116291180A (en) * | 2023-04-10 | 2023-06-23 | 中煤科工集团重庆研究院有限公司 | Double-motor power head for drilling machine and use method thereof |
| CN116576166A (en) * | 2023-05-10 | 2023-08-11 | 中铁工程装备集团有限公司 | Main driving system and control method thereof |
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| CN105416199A (en) * | 2014-07-24 | 2016-03-23 | 冯春魁 | Vehicle operation monitoring, parameter measuring and calculating, overload monitoring method and system |
| WO2022236064A2 (en) * | 2021-05-06 | 2022-11-10 | Strong Force Iot Portfolio 2016, Llc | Quantum, biological, computer vision, and neural network systems for industrial internet of things |
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