CN116512273A - Intelligent motion control method and system for inspection robot - Google Patents
Intelligent motion control method and system for inspection robot Download PDFInfo
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- CN116512273A CN116512273A CN202310621581.XA CN202310621581A CN116512273A CN 116512273 A CN116512273 A CN 116512273A CN 202310621581 A CN202310621581 A CN 202310621581A CN 116512273 A CN116512273 A CN 116512273A
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J13/00—Controls for manipulators
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The invention provides an intelligent control method and system for movement of a patrol robot, which relate to the technical field of data processing, and are used for constructing a three-dimensional topological structure of a target power plant, inputting patrol demand information into a patrol planning model, outputting patrol execution information as a response execution target, collecting real-time patrol data for primary identification early warning, executing data warehouse entry for deep analysis early warning, comprehensively analyzing to generate an operation and maintenance execution task, determining a secondary patrol point, executing the operation and maintenance execution task, planning a re-examination path and carrying out re-examination control.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent motion control method and system for a patrol robot.
Background
With the development and market release of the inspection robot, the inspection robot gradually replaces manual inspection operation, the inspection risk is not considered, meanwhile, the inspection standardization and the inspection digitization can be guaranteed, and the inspection energy efficiency is improved. At present, the control of the inspection robot is performed in a directional manner based on a pre-configured inspection execution mechanism, and the inspection scene and the inspection influencing factors are considered for a long time so as to influence the final inspection effect.
In the prior art, the inspection control method for the inspection robot is not flexible enough, and the inspection execution effect is limited due to the defect of insufficient analysis depth of inspection planning and insufficient scene combination degree and inspection integrity.
Disclosure of Invention
The application provides an intelligent motion control method and system for a patrol robot, which are used for solving the technical problems that in the prior art, the patrol control method for the patrol robot is not flexible enough, the analysis depth of patrol planning is not enough, the scene combination degree and the patrol integrity are not enough, and the patrol execution effect is limited.
In view of the above problems, the present application provides a method and a system for intelligently controlling movement of a patrol robot.
In a first aspect, the present application provides a method for intelligently controlling movement of a patrol robot, where the method includes:
basic information of a target power plant is collected, and a three-dimensional topological structure is generated;
building a routing inspection planning model, inputting routing inspection requirement information into the routing inspection planning model, and outputting routing inspection execution information, wherein the three-dimensional topological structure is embedded in the routing inspection planning model, the routing inspection execution information comprises an inspection period and an inspection route, and the inspection route is provided with an inspection mode identifier;
taking the inspection period and the inspection route as response execution targets, collecting real-time inspection data and carrying out primary identification early warning, wherein the primary identification early warning is judged based on a directional early warning standard set by a burning program;
executing data warehouse entry on the real-time inspection data, and carrying out deep analysis early warning on the data;
generating an operation and maintenance execution task based on the primary identification early warning and the deep analysis early warning by field and background linkage, and determining a secondary inspection point;
and executing the operation and maintenance execution task, and performing rechecking control of the inspection robot by taking the shortest route as a response target to perform rechecking path planning based on the secondary inspection point.
In a second aspect, the present application provides a motion intelligent control system of a patrol robot, the system comprising:
the structure generation module is used for collecting basic information of a target power plant and generating a three-dimensional topological structure;
the inspection planning module is used for building an inspection planning model, inputting inspection requirement information into the inspection planning model and outputting inspection execution information, wherein the three-dimensional topological structure is embedded in the inspection planning model, the inspection execution information comprises an inspection period and an inspection route, and the inspection route is provided with an inspection mode mark;
the primary identification early warning module is used for collecting real-time inspection data and carrying out primary identification early warning, wherein the primary identification early warning is judged based on a directional early warning standard set by a burning program;
the deep analysis early warning module is used for executing data warehousing on the real-time inspection data and carrying out deep analysis early warning on the data;
the inspection point determining module is used for generating an operation and maintenance executing task based on the primary identification early warning and the deep analysis early warning by linking with a background on site and determining a secondary inspection point;
and the rechecking control module is used for executing the operation and maintenance execution task, carrying out rechecking path planning by taking the shortest route as a response target based on the secondary inspection point, and carrying out rechecking control of the inspection robot.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the intelligent motion control method for the inspection robot, basic information of a target power plant is collected, and a three-dimensional topological structure is generated; constructing a routing inspection planning model, inputting routing inspection requirement information into the routing inspection planning model, outputting routing inspection execution information, wherein the routing inspection route is provided with routing inspection mode identifiers, taking the routing inspection period and the routing inspection route as response execution targets, acquiring real-time routing inspection data and performing primary identification early warning, judging the primary identification early warning based on directional early warning standards set by a burning program, further executing data warehouse entry on the real-time routing inspection data, and performing deep analysis early warning of the data; based on the primary identification early warning and the deep profiling early warning, generating an operation and maintenance execution task, determining a secondary inspection point, executing the operation and maintenance execution task, carrying out rechecking path planning by taking a shortest route as a response target based on the secondary inspection point, and carrying out rechecking control of the inspection robot, thereby solving the technical problems of insufficient analysis depth, insufficient scene combination degree and inspection integrity of the inspection planning, limited inspection execution effect, carrying out adaptive inspection planning aiming at inspection requirement modeling, ensuring scene suitability, carrying out timely analysis processing and reasonable tuning aiming at emergency conditions in an inspection process, and realizing flexible and accurate management and control of regional inspection in the prior art.
Drawings
Fig. 1 is a schematic flow chart of a method for controlling movement of an inspection robot;
fig. 2 is a schematic diagram of an acquisition flow for adjusting a routing inspection route in the intelligent motion control method of the routing inspection robot;
fig. 3 is a schematic diagram of a self-warning analysis execution flow in the intelligent motion control method of the inspection robot;
fig. 4 is a schematic structural diagram of a motion intelligent control system of a patrol robot.
Reference numerals illustrate: the system comprises a structure generation module 11, a patrol planning module 12, a primary identification early warning module 13, a deep profiling early warning module 14, a patrol point determination module 15 and a review control module 16.
Detailed Description
According to the intelligent control method and system for the movement of the inspection robot, basic information of a target power plant is collected, a three-dimensional topological structure is generated, inspection requirement information is input into an inspection planning model, inspection execution information is output as a response execution target, real-time inspection data is collected and subjected to primary identification early warning, data storage is performed on the real-time inspection data to perform deep analysis early warning, comprehensive analysis is performed to generate an operation and maintenance execution task, a secondary inspection point is determined, the operation and maintenance execution task is performed, a recheck path is planned, and recheck control is performed.
Example 1
As shown in fig. 1, the present application provides a motion intelligent control method of a patrol robot, where the method includes:
step S100: basic information of a target power plant is collected, and a three-dimensional topological structure is generated;
specifically, with the development and market release of the inspection robot, the inspection robot gradually replaces manual inspection operation, the inspection risk is not considered, meanwhile, the inspection standardization and digitization can be guaranteed, and the inspection energy efficiency is improved. The intelligent motion control method for the inspection robot is applied to inspection control of a power plant, comprehensive inspection control of the whole field is performed on a target power plant, and the inspection requirement of the power plant is high and certain inspection risk exists, so that inspection requirements can be met to the greatest extent by running inspection operation based on the inspection robot.
Specifically, the target power plant is a power plant of the inspection robot to be subjected to inspection operation, basic information such as configuration equipment and distribution of the target power plant is collected, a three-dimensional topological structure which is identical to the target power plant in step is built, the three-dimensional topological structure is used as a space visualization architecture of the target power plant, the space requirement of inspection monitoring can be completely covered, and the determination of inspection details is facilitated. Preferably, regional inspection grades are classified for the target power plant, for example, the target power plant is divided into a plurality of subareas for inspection risks, inspection difficulties and the like, inspection grade identification is performed in the three-dimensional topological network, and the follow-up targeted inspection planning is facilitated.
Step S200: building a routing inspection planning model, inputting routing inspection requirement information into the routing inspection planning model, and outputting routing inspection execution information, wherein the three-dimensional topological structure is embedded in the routing inspection planning model, the routing inspection execution information comprises an inspection period and an inspection route, and the inspection route is provided with an inspection mode identifier;
further, the inspection route has an inspection mode identifier, and step S200 of the present application further includes:
step S210: the statistical acquisition of multidimensional inspection modes comprises a long start inspection mode and a fixed start inspection mode, the configuration equipment of different inspection modes is different,
step S220: based on the multidimensional routing inspection mode, routing inspection requirement matching is conducted in combination with the routing inspection route, and a node matching mode is determined;
step S230: and identifying the routing inspection route based on the node matching mode.
The method comprises the steps of constructing a multi-level network layer, including a route planning layer, an information adjustment layer and a mode configuration layer, determining a level execution logic to perform network layer training, embedding the three-dimensional map structure into the three-dimensional map structure for performing auxiliary planning analysis, performing level connection association on the route planning layer, the information adjustment layer and the mode configuration layer to generate a routing inspection planning model, further calling a historical routing inspection record, including sample routing inspection requirements and sample routing inspection execution information, inputting the sample routing inspection requirements into the routing inspection planning model to perform model verification, performing mapping check on a model verification result and the routing inspection execution information to perform analysis accuracy check of the model, and if the constructed analysis accuracy of the routing inspection planning model does not reach the standard, preferentially extracting training samples based on the historical routing inspection record, training the routing inspection planning model to converge, and obtaining the constructed routing inspection planning model.
Furthermore, the target power plant is subjected to acquisition of inspection time difference, inspection requirement, inspection data type and the like at each position, and statistics is regular to be used as the inspection requirement information. Inputting the routing inspection requirement information into the routing inspection planning model, determining a primary routing inspection route by combining the three-dimensional topological structure, transmitting the primary routing inspection route to the information adjustment layer, configuring the routing inspection angles, the routing inspection time length and the like of each node of a path by combining the routing inspection requirement, carrying out refined adjustment on the primary routing inspection route, for example, carrying out omnibearing multi-angle routing inspection analysis on certain power plant equipment, further transmitting the primary routing inspection route to the mode configuration layer, and carrying out routing inspection mode configuration on routing inspection position nodes. The inspection data types are different, the corresponding inspection modes are different, for example, for areas with higher risk degree, the multi-dimensional data are required to be subjected to cooperative judgment, and the start and stop control of the inspection modes is performed based on the inspection requirements.
Specifically, a multidimensional inspection mode, such as a video inspection mode, is obtained, inspection execution is performed based on the assembled image acquisition equipment, and the video inspection mode can be used as a long-start inspection mode and penetrates through the whole inspection flow; the vibration inspection mode, the temperature inspection mode and the like can be performed based on the assembled corresponding sensing equipment, and can be used as a fixed-start inspection mode to be activated and started when inspection requirements exist. And aiming at nodes at different positions of the routing inspection route, respectively performing routing inspection requirement matching, such as the operation efficiency, the temperature and the like of power plant equipment, traversing the multi-dimensional routing inspection mode to perform matching, and determining the node matching mode. And carrying out positioning identification of the node matching mode in the routing inspection route, simultaneously carrying out collaborative analysis based on the routing inspection time difference of each position, determining the routing inspection period meeting the routing inspection time difference, and carrying out model output by taking the routing inspection route with the routing inspection mode identification and the routing inspection period as the routing inspection execution information. The inspection execution analysis is performed through modeling, so that the analysis efficiency can be effectively improved, and the accuracy and objectivity of an analysis result are ensured.
Step S300: taking the inspection period and the inspection route as response execution targets, collecting real-time inspection data and carrying out primary identification early warning, wherein the primary identification early warning is judged based on a directional early warning standard set by a burning program;
step S400: executing data warehouse entry on the real-time inspection data, and carrying out deep analysis early warning on the data;
specifically, a single routing inspection starting time node is determined based on the routing inspection period, the routing inspection route is used as a routing inspection execution standard, the routing inspection robot is controlled to execute regional routing inspection, the starting and stopping control of standby inspection equipment is performed based on the routing inspection mode of routing inspection route identification, the standby inspection equipment is monitoring acquisition equipment meeting the routing inspection requirement, such as video acquisition equipment and the like, corresponding real-time routing inspection data are acquired along with the routing inspection process of the routing inspection robot, basic judgment standards capable of directly performing abnormality judgment, such as the temperature of a routing inspection target and the like, an abnormal temperature threshold is added into the directional early warning standard, the directional early warning standard is burnt into an initial program of the routing inspection robot, and the directional early warning standard is connected with the standby inspection equipment for data interaction analysis. And calling the directional early warning standard for matching and checking aiming at the real-time inspection data, and carrying out primary identification early warning aiming at the real-time inspection data which does not meet the corresponding directional early warning standard, wherein the primary identification early warning is the data early warning aiming at real-time inspection of the inspection robot. Furthermore, data storage is performed on the real-time inspection data, an auxiliary data processing tool performs further abnormality analysis and tracing positioning, such as image enhancement processing, convolution feature recognition and the like, abnormal inspection data are determined, and deep analysis early warning is performed. And (3) performing batch early warning, and controlling early warning repair time.
Further, as shown in fig. 2, step S300 of the present application further includes:
step S310-1: receiving real-time inspection requirements, and generating special inspection tasks;
step S320-1: performing timeliness limiting on the special routing task, traversing the routing inspection route for matching, and determining an adaptive adjustment node by combining a real-time routing inspection condition;
step S330-1: and inserting the special routing task at the adaptive adjustment node, and determining an adjustment routing path.
Specifically, in the routing inspection process, a temporarily received inspection task may exist, and the inspection task is arranged according to timeliness and inspection live of the inspection task. Specifically, the real-time inspection requirement is temporarily received information to be executed, semantic analysis and conversion are performed on the information to be executed, the special inspection task is generated, required task execution time is determined, and the special inspection task is identified. And further performing timeliness limiting on the special routing task, namely, traversing the routing path for matching the time node of limiting the special routing task, determining the routing path node with the shortest distance to the area where the special routing task is located, determining the starting time of the special routing task by combining the timeliness limiting and the task execution time, and if the special routing task is an urgent task, comprehensively regulating and controlling by combining the current real-time routing condition, wherein the optimal routing node meeting the routing requirement is used as the adaptive regulating node. And positioning the adaptive adjustment node in the routing inspection route, and inserting the special routing inspection task as the adjustment routing inspection route. And the irregular insertion of the special patrol task is determined according to the real-time patrol requirement.
Further, step S300 of the present application further includes:
step S310-2: performing path obstacle sensing based on the routing inspection route, and determining target obstacle information;
step S320-2: performing built-in database obstacle avoidance retrieval on the target obstacle information, and determining a safe triggering distance and an obstacle avoidance mode;
step S330-2: if the obstacle avoidance mode is bypass obstacle avoidance, determining an initial adjusting point based on the safety trigger distance, and acquiring an obstacle avoidance path to position and cover the inspection route, wherein the obstacle avoidance path comprises an adjustment path, an inspection speed and an inspection direction;
step S340-2: and if the obstacle avoidance mode is non-detour obstacle avoidance, performing pause control based on the safety trigger distance.
Further, if the obstacle avoidance mode is non-detouring obstacle avoidance, performing pause control based on the safety trigger distance, step S340-2 of the present application further includes:
step S341-2: if the obstacle avoidance mode is non-detour obstacle avoidance, generating obstacle early warning information and determining whether the safety trigger distance is met;
step S342-2: if the safety trigger distance is not met, performing scram control on the inspection robot;
step S343-2: and if the safe trigger distance is met, calculating deceleration and executing stop-reducing control on the inspection robot.
Specifically, in the inspection course, path obstacles, such as object moving, inspection topography, etc., inevitably exist, and the path obstacles in the real-time inspection course need to be effectively avoided in time. Specifically, the inspection robot is equipped with an infrared sensing device, obstacle sensing at a predetermined distance can be performed in real time in the inspection route, and basic information such as the size of a path obstacle is determined and used as the target obstacle information. The built-in database is obstacle avoidance information stored in the built-in chip and comprises obstacle types, obstacle avoidance mode selection and the like, and the built-in database can be periodically updated in a database learning way along with obstacle avoidance execution of the inspection robot. And aiming at the target obstacle information, carrying out obstacle avoidance search in the built-in database, determining the route distance between the inspection robot and the target obstacle in the optimal obstacle avoidance state, taking the route distance as the safe trigger distance, scanning the area where the target obstacle is located, and matching with an adaptive obstacle avoidance mode.
Further, the obstacle avoidance mode is judged, if the target obstacle is smaller and the residual width of the path can meet the situation of the inspection robot, or a turnout exists in a scanning area of the target obstacle to support the detour of the inspection robot, the obstacle avoidance mode is determined to be detour obstacle avoidance, an inspection route position point which meets the safety trigger distance and is far away from the target obstacle is further determined to serve as the initial adjusting point, inspection route adjustment in the safety trigger distance is carried out by combining the target obstacle information and area scanning information, the adjustment route, the inspection speed and the inspection direction are adopted as the obstacle avoidance route, matching positioning is carried out on the obstacle avoidance route, and interception of an original route and coverage of the obstacle avoidance route are carried out; and if the safety trigger distance is not satisfied, for example, corner position obstacle avoidance and the like, carrying out inspection emergency stop and in-situ adjustment control.
If the inspection detour condition does not exist, determining that the obstacle avoidance mode is non-detour obstacle avoidance, such as route blockage, step existence and other obstacles, generating the obstacle early warning information, namely warning response of the front obstacle, and manually adjusting the obstacle early warning information. Further judging whether the safety trigger distance is met, if the safety trigger distance is not met, indicating that collision risk exists in normal stop-down operation, and executing scram control on the normal stop-down operation, wherein only special conditions are started because the scram control has a certain influence on the service life of the inspection robot and the like; if the safety trigger distance is met, determining a deceleration path based on the real-time inspection position of the inspection robot and the position of the target obstacle, calculating deceleration by combining the real-time inspection speed of the inspection robot, executing stop-reducing control on the inspection robot, and starting inspection after the target obstacle is processed. And the inspection influence factors of the inspection robot are timely and effectively analyzed and processed, so that the normal inspection process is prevented from being influenced.
Further, step S300 of the present application further includes:
step S310-3: setting a charging threshold, wherein the charging threshold is determined based on the real-time distance between the inspection robot and a workstation and is dynamically adjusted along with the real-time positioning of the inspection robot;
step S320-3: generating a patrol interruption instruction if the real-time electric quantity of the patrol robot is smaller than or equal to the charging threshold value;
step S330-3: and controlling the inspection robot to automatically perform the dispatch execution based on the inspection interrupt instruction, wherein the inspection interrupt position is used as a later inspection starting point.
Specifically, in the inspection process of the inspection robot, electric quantity control needs to be strictly performed, the limit electric quantity of the inspection robot is determined, namely, the minimum electric quantity which does not cause equipment damage is determined based on the distance between the real-time inspection position of the inspection robot and the workstation, the distance is a driving distance and a nonlinear distance, the workstation is a charging area of the inspection robot, the sum of the limit electric quantity and the dispatching power consumption is used as a charging threshold value based on the determination of the dispatching power consumption, and the charging threshold value fluctuates in real time along with the inspection process of the inspection robot, so that the inspection abnormality caused by insufficient electric quantity can be effectively avoided. The charging threshold has real-time performance, the real-time electric quantity of the inspection robot is checked with the charging threshold, and if the real-time electric quantity is larger than the charging threshold updated in real time, the inspection process is normally carried out; and if the power consumption is smaller than or equal to the charging threshold, indicating that the power consumption is insufficient and only the power consumption can be saved, and generating the patrol interruption instruction. And along with the receiving of the inspection interrupt instruction, controlling the inspection robot to execute automatic dispatch, returning to the workstation to complete charging, taking the interrupt position as the starting point of the subsequent inspection, and returning to continue inspection after the charging is completed.
Step S500: generating an operation and maintenance execution task based on the primary identification early warning and the deep analysis early warning by field and background linkage, and determining a secondary inspection point;
step S600: and executing the operation and maintenance execution task, and performing rechecking control of the inspection robot by taking the shortest route as a response target to perform rechecking path planning based on the secondary inspection point.
Specifically, the primary identification early warning is an on-site inspection early warning of the inspection robot, the deep analysis early warning is a later data anomaly analysis traceability early warning, the operation and maintenance execution task is generated based on the primary identification early warning and the deep analysis early warning, the operation and maintenance execution task is provided with a time sequence identifier, and an early warning position is used as the secondary inspection point. Further, the operation and maintenance execution task is executed, after early warning restoration is completed, the secondary inspection points are subjected to scheduling connection, the shortest route is taken as a response target by combining the inspection planning model, a re-inspection route is determined, the re-inspection route is taken as an inspection standard, the inspection robot is controlled to execute inspection, and further analysis is performed on inspection collected data.
Further, as shown in fig. 3, the present application further includes step S700, including:
step S710: generating a self-checking standard list based on the normal execution condition of the inspection robot;
step S720: embedding the self-checking standard list into a central control module of the inspection robot;
step S730: based on a preset time period, the inspection robot periodically executes operation self-checking, and generates a self-checking data set, wherein the operation self-checking comprises self-checking of standby equipment and operation self-checking of the whole machine;
step S740: mapping and checking the self-checking standard list and the self-checking data set to generate abnormal early warning information;
step S750: and carrying out self-warning of the inspection robot based on the abnormal early warning information, wherein the early warning execution modes of different parts and different early warning grades are different.
Specifically, in order to ensure the inspection accuracy of the inspection robot, the operation self-inspection control of the inspection robot is performed regularly. The standard inspection state of the inspection robot is counted, the standard inspection state comprises path control accuracy, standby inspection equipment control accuracy and the like, the standard inspection state is used as the normal execution condition, the controllable deviation of the normal execution condition is collected, mapping association is conducted on the normal execution condition and the controllable deviation, the self-inspection standard list is generated through integration, and the self-inspection standard list is used for performing self-inspection reference of inspection operation of the inspection robot. And embedding the self-checking standard list into a central control module of the inspection robot, wherein the central control module is a comprehensive control area of the inspection robot. And setting the preset time period, namely the execution time limit interval for running self-checking, and carrying out self-defining setting by referring to the historical running record of the inspection robot.
Based on the preset time period, taking the self-check of the checked equipment and the running self-check of the whole machine as self-check execution directions, controlling the inspection robot to execute the running self-check, and carrying out data source identification of self-check data to generate the self-check data set. Further, mapping correspondence is carried out on the self-checking data set and the self-checking standard list, whether the self-checking data set meets a controllability deviation range corresponding to the self-checking standard list is judged, if the self-checking data set does not meet a controllability deviation range corresponding to the self-checking standard list, the source tracing is carried out on abnormal data, and an early warning part, an early warning type and an early warning level are determined and used as the abnormal early warning information. The self-warning of the inspection robot is carried out aiming at the abnormal early warning information, specifically, different early warning modes are configured aiming at different early warning information, for example, various early warning states such as bell, flashing light and the like are configured, and the diversity of colors, frequencies and the like is included so as to represent the differentiation of specific early warning, so that early warning distinction can be carried out.
Example two
Based on the same inventive concept as the intelligent motion control method of the inspection robot in the foregoing embodiment, as shown in fig. 4, the present application provides an intelligent motion control system of the inspection robot, where the system includes:
the structure generation module 11 is used for collecting basic information of a target power plant and generating a three-dimensional topological structure;
the inspection planning module 12 is configured to build an inspection planning model, input inspection requirement information into the inspection planning model, and output inspection execution information, where the three-dimensional topological structure is embedded in the inspection planning model, and the inspection execution information includes an inspection period and an inspection route, and the inspection route has an inspection mode identifier;
the primary identification early warning module 13 is used for taking the inspection period and the inspection route as response execution targets, collecting real-time inspection data and carrying out primary identification early warning, wherein the primary identification early warning is judged based on a directional early warning standard set by a burning program;
the deep analysis early warning module 14 is used for executing data warehousing on the real-time inspection data by the deep analysis early warning module 14 so as to perform deep analysis early warning on the data;
the inspection point determining module 15 is used for generating an operation and maintenance executing task based on the primary identification early warning and the deep analysis early warning by linking the scene with a background and determining a secondary inspection point;
and the rechecking control module 16 is used for executing the operation and maintenance execution task, and performing rechecking control on the inspection robot by taking the shortest route as a response target to perform rechecking path planning based on the secondary inspection point.
Further, the system further comprises:
the inspection mode acquisition module is used for statistically acquiring multidimensional inspection modes, comprising a long-start inspection mode and a fixed-start inspection mode, the configuration standby inspection equipment of different inspection modes is different,
the routing inspection requirement matching module is used for conducting routing inspection requirement matching by combining the routing inspection route based on the multi-dimensional routing inspection mode, and determining a node matching mode;
and the path identification module is used for identifying the routing inspection route based on the node matching mode.
Further, the system further comprises:
the task generation module is used for receiving real-time patrol requirements and generating special patrol tasks;
the adjusting node determining module is used for limiting the time effect of the special routing task, traversing the routing path for matching, and determining an adaptive adjusting node by combining the real-time routing condition;
and the routing inspection route adjusting module is used for inserting the special routing inspection task at the adaptive adjusting node and determining and adjusting the routing inspection route.
Further, the system further comprises:
the obstacle sensing module is used for sensing path obstacle based on the inspection route and determining target obstacle information;
the obstacle avoidance retrieval module is used for carrying out built-in database obstacle avoidance retrieval on the target obstacle information and determining a safe trigger distance and an obstacle avoidance mode;
the obstacle avoidance path acquisition module is used for determining an initial adjusting point based on the safety trigger distance if the obstacle avoidance mode is detouring obstacle avoidance, and acquiring an obstacle avoidance path to position and cover the inspection route, wherein the obstacle avoidance path comprises an adjusting path, an inspection speed and an inspection direction;
and the pause control module is used for carrying out pause control based on the safety trigger distance if the obstacle avoidance mode is non-detour obstacle avoidance.
Further, the system further comprises:
the distance judging module is used for generating obstacle early warning information and determining whether the safety trigger distance is met if the obstacle avoidance mode is non-detour obstacle avoidance;
the emergency stop control module is used for executing emergency stop control on the inspection robot if the safety trigger distance is not met;
and the stop-reducing control module is used for calculating deceleration and executing stop-reducing control on the inspection robot if the safety trigger distance is met.
Further, the system further comprises:
the threshold setting module is used for setting a charging threshold, wherein the charging threshold is determined based on the real-time distance between the inspection robot and the workstation and is dynamically adjusted along with the real-time positioning of the inspection robot;
the electric quantity judging module is used for generating a patrol interruption instruction if the real-time electric quantity of the patrol robot is smaller than or equal to the charging threshold value;
and the dispatch control module is used for controlling the inspection robot to carry out automatic dispatch execution based on the inspection interrupt instruction, wherein the inspection interrupt position is used as a subsequent inspection starting point.
Further, the system further comprises:
the list generation module is used for generating a self-checking standard list based on the normal execution condition of the inspection robot;
the list embedding module is used for embedding the self-checking standard list into the central control module of the inspection robot;
the operation self-checking module is used for periodically executing operation self-checking by the inspection robot based on a preset time period to generate a self-checking data set, wherein the operation self-checking comprises self-checking of standby equipment and self-checking of whole machine operation;
the early warning information generation module is used for carrying out mapping check on the self-checking standard list and the self-checking data set to generate abnormal early warning information;
and the warning module is used for carrying out self warning on the inspection robot based on the abnormal early warning information, wherein the early warning execution modes of different parts and different early warning grades are different.
Through the foregoing detailed description of the motion intelligent control method of the inspection robot, those skilled in the art can clearly know the motion intelligent control method and system of the inspection robot in this embodiment, and for the apparatus disclosed in the embodiments, the description is relatively simple because it corresponds to the method disclosed in the embodiments, and relevant places refer to the description of the method section.
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.
Claims (8)
1. The intelligent motion control method for the inspection robot is characterized by comprising the following steps:
basic information of a target power plant is collected, and a three-dimensional topological structure is generated;
building a routing inspection planning model, inputting routing inspection requirement information into the routing inspection planning model, and outputting routing inspection execution information, wherein the three-dimensional topological structure is embedded in the routing inspection planning model, the routing inspection execution information comprises an inspection period and an inspection route, and the inspection route is provided with an inspection mode identifier;
taking the inspection period and the inspection route as response execution targets, collecting real-time inspection data and carrying out primary identification early warning, wherein the primary identification early warning is judged based on a directional early warning standard set by a burning program;
executing data warehouse entry on the real-time inspection data, and carrying out deep analysis early warning on the data;
generating an operation and maintenance execution task based on the primary identification early warning and the deep analysis early warning by field and background linkage, and determining a secondary inspection point;
and executing the operation and maintenance execution task, and performing rechecking control of the inspection robot by taking the shortest route as a response target to perform rechecking path planning based on the secondary inspection point.
2. The method of claim 1, wherein the inspection route is provided with an inspection pattern identification, the method comprising:
the statistical acquisition of multidimensional inspection modes comprises a long start inspection mode and a fixed start inspection mode, the configuration equipment of different inspection modes is different,
based on the multidimensional routing inspection mode, routing inspection requirement matching is conducted in combination with the routing inspection route, and a node matching mode is determined;
and identifying the routing inspection route based on the node matching mode.
3. The method of claim 1, wherein the method comprises:
receiving real-time inspection requirements, and generating special inspection tasks;
performing timeliness limiting on the special routing task, traversing the routing inspection route for matching, and determining an adaptive adjustment node by combining a real-time routing inspection condition;
and inserting the special routing task at the adaptive adjustment node, and determining an adjustment routing path.
4. A method as claimed in claim 3, wherein the method comprises:
performing path obstacle sensing based on the routing inspection route, and determining target obstacle information;
performing built-in database obstacle avoidance retrieval on the target obstacle information, and determining a safe triggering distance and an obstacle avoidance mode;
if the obstacle avoidance mode is bypass obstacle avoidance, determining an initial adjusting point based on the safety trigger distance, and acquiring an obstacle avoidance path to position and cover the inspection route, wherein the obstacle avoidance path comprises an adjustment path, an inspection speed and an inspection direction;
and if the obstacle avoidance mode is non-detour obstacle avoidance, performing pause control based on the safety trigger distance.
5. The method of claim 4, wherein if the obstacle avoidance mode is non-detour obstacle avoidance, performing pause control based on the safety trigger distance, the method comprising:
if the obstacle avoidance mode is non-detour obstacle avoidance, generating obstacle early warning information and determining whether the safety trigger distance is met;
if the safety trigger distance is not met, performing scram control on the inspection robot;
and if the safe trigger distance is met, calculating deceleration and executing stop-reducing control on the inspection robot.
6. The method of claim 1, wherein the method comprises:
setting a charging threshold, wherein the charging threshold is determined based on the real-time distance between the inspection robot and a workstation and is dynamically adjusted along with the real-time positioning of the inspection robot;
generating a patrol interruption instruction if the real-time electric quantity of the patrol robot is smaller than or equal to the charging threshold value;
and controlling the inspection robot to automatically perform the dispatch execution based on the inspection interrupt instruction, wherein the inspection interrupt position is used as a later inspection starting point.
7. The method of claim 1, wherein the method comprises:
generating a self-checking standard list based on the normal execution condition of the inspection robot;
embedding the self-checking standard list into a central control module of the inspection robot;
based on a preset time period, the inspection robot periodically executes operation self-checking, and generates a self-checking data set, wherein the operation self-checking comprises self-checking of standby equipment and operation self-checking of the whole machine;
mapping and checking the self-checking standard list and the self-checking data set to generate abnormal early warning information;
and carrying out self-warning of the inspection robot based on the abnormal early warning information, wherein the early warning execution modes of different parts and different early warning grades are different.
8. An intelligent motion control system for a patrol robot, the system comprising:
the structure generation module is used for collecting basic information of a target power plant and generating a three-dimensional topological structure;
the inspection planning module is used for building an inspection planning model, inputting inspection requirement information into the inspection planning model and outputting inspection execution information, wherein the three-dimensional topological structure is embedded in the inspection planning model, the inspection execution information comprises an inspection period and an inspection route, and the inspection route is provided with an inspection mode mark;
the primary identification early warning module is used for collecting real-time inspection data and carrying out primary identification early warning, wherein the primary identification early warning is judged based on a directional early warning standard set by a burning program;
the deep analysis early warning module is used for executing data warehousing on the real-time inspection data and carrying out deep analysis early warning on the data;
the inspection point determining module is used for generating an operation and maintenance executing task based on the primary identification early warning and the deep analysis early warning by linking with a background on site and determining a secondary inspection point;
and the rechecking control module is used for executing the operation and maintenance execution task, carrying out rechecking path planning by taking the shortest route as a response target based on the secondary inspection point, and carrying out rechecking control of the inspection robot.
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| CN116720854A (en) * | 2023-08-11 | 2023-09-08 | 成都煦联得节能科技有限公司 | Equipment coordination control method and system based on intelligent patrol |
| CN116929373A (en) * | 2023-09-15 | 2023-10-24 | 江苏宁昆机器人智能科技有限公司 | Path generation method and system of fire control reconnaissance robot |
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| CN116720854A (en) * | 2023-08-11 | 2023-09-08 | 成都煦联得节能科技有限公司 | Equipment coordination control method and system based on intelligent patrol |
| CN116720854B (en) * | 2023-08-11 | 2023-11-03 | 成都煦联得节能科技有限公司 | Equipment coordination control method and system based on intelligent patrol |
| CN116929373A (en) * | 2023-09-15 | 2023-10-24 | 江苏宁昆机器人智能科技有限公司 | Path generation method and system of fire control reconnaissance robot |
| CN116929373B (en) * | 2023-09-15 | 2023-12-12 | 江苏宁昆机器人智能科技有限公司 | Path generation method and system of fire control reconnaissance robot |
| CN117197770A (en) * | 2023-11-06 | 2023-12-08 | 深圳市金固祥科技有限公司 | Inspection complete flow data supervision system and method based on Internet of things |
| CN117197770B (en) * | 2023-11-06 | 2024-02-23 | 深圳市金固祥科技有限公司 | Inspection complete flow data supervision system and method based on Internet of things |
| CN117428774A (en) * | 2023-11-23 | 2024-01-23 | 中国船舶集团有限公司第七一六研究所 | Industrial robot control method and system for ship inspection |
| CN117733819A (en) * | 2024-02-21 | 2024-03-22 | 太原工业学院 | An intelligent inspection robot operating method and device for power plants |
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