CN115129430A - Robot remote control instruction issuing method and system based on 5g network - Google Patents
Robot remote control instruction issuing method and system based on 5g network Download PDFInfo
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Abstract
The invention provides a robot remote control instruction issuing method and system based on a 5g network, relating to the technical field of robot remote control, wherein the method comprises the following steps: modifying the variables of the running program based on the 5g network platform; acquiring an operation parameter set; acquiring an operation state set; constructing a robot digital twin platform; acquiring an operation parameter random adjustment set and an operation state random adjustment set; acquiring a panel display information set; generating a remote diagnosis report of the robot; and obtaining a remote diagnosis result. The technical problem that equipment maintenance cannot be timely carried out on the robot due to the fact that the robot remote control instruction is not timely sent is solved, the technical effects that on the basis of a 5g network platform, the robot remote control instruction is timely sent, operation is assisted through a cloud processor, operation efficiency is improved, fault nodes are quickly located, a professional engineer is assisted in troubleshooting, fault processing time is shortened, and equipment maintenance is timely carried out on the robot are achieved.
Description
Technical Field
The invention relates to the technical field of robot remote control, in particular to a robot remote control instruction issuing method and system based on a 5g network.
Background
The robot is an anthropomorphic electromechanical device, has the capability of quickly reacting, analyzing and judging the environment state, has the capability of continuously working for a long time, has high accuracy and resists severe environment, is important production and service equipment in industry and non-industry, and is also indispensable automation equipment in the technical field of advanced manufacturing.
However, the remote control instruction of the robot is not sent timely, the control delay of the robot is easily caused, the cognition of the field management personnel of the robot on the state parameters of the robot is limited, and the potential problems of the robot existing in the real-time operation and maintenance system of the robot cannot be effectively monitored and early warned.
The technical problem that equipment maintenance cannot be timely performed on the robot due to the fact that the robot remote control instruction is not timely sent exists in the prior art.
Disclosure of Invention
The application provides a robot remote control instruction issuing method and system based on a 5g network, the technical problem that the robot remote control instruction cannot be timely sent to cause the problem that equipment maintenance cannot be timely carried out on the robot is solved, the purpose that the robot remote control instruction is timely sent based on the 5g network platform is achieved, operation is assisted through a cloud processor, operation efficiency is improved, fault nodes are quickly located, a professional engineer is assisted in troubleshooting, fault processing time is shortened, and the technical effect of equipment maintenance is timely carried out on the robot.
In view of the above problems, the present application provides a robot remote control instruction issuing method and system based on a 5g network.
In a first aspect of the present application, a robot remote control instruction issuing method based on a 5g network is provided, where the method includes: modifying the variables of the running program of the robot based on a 5g network platform by combining a remote diagnosis calling instruction; synchronously acquiring the running parameters of the robot and acquiring a running parameter set; monitoring the running state of the robot in real time by using a monitoring camera to obtain a running state set; importing the operation parameter set and the operation state set into simulation software, performing index reduction setting, and building a robot digital twin platform; randomly changing the number of variables of the robot, and acquiring an operation parameter random adjustment set and an operation state random adjustment set through the remote diagnosis call instruction; synchronously recording panel display information of the robot, and acquiring a panel display information set; integrating the running parameter random adjustment set, the running state random adjustment set and a panel display information set to generate a remote diagnosis report of the robot; and sending the remote diagnosis report to an equipment end based on the robot digital twin platform to obtain a remote diagnosis result.
In a second aspect of the present application, a robot remote control instruction issuing system based on a 5g network is provided, where the system includes: the variable modification unit is used for modifying the variable of the running program of the robot based on a 5g network platform by combining a remote diagnosis call instruction; the robot control system comprises a parameter acquisition unit, a parameter processing unit and a control unit, wherein the parameter acquisition unit is used for synchronously acquiring the operation parameters of the robot and acquiring an operation parameter set; the operation state acquisition unit is used for monitoring the operation state of the robot in real time by using the monitoring camera to acquire an operation state set; the digital platform building unit is used for importing the operation parameter set and the operation state set into simulation software, carrying out index reduction setting and building a robot digital twin platform; the random adjusting unit is used for randomly changing the number of variables of the robot and acquiring a running parameter random adjusting set and a running state random adjusting set through the remote diagnosis calling instruction; the synchronous recording unit is used for synchronously recording panel display information of the robot and acquiring a panel display information set; the diagnosis report generating unit is used for integrating the operation parameter random adjusting set, the operation state random adjusting set and the panel display information set to generate a remote diagnosis report of the robot; and the diagnosis result acquisition unit is used for sending the remote diagnosis report to an equipment side based on the digital twin platform of the robot to acquire a remote diagnosis result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
because a 5 g-based network platform is adopted, and a remote diagnosis call instruction is combined, the variable of the running program of the robot is modified; synchronously acquiring the running parameters of the robot and acquiring a running parameter set; monitoring the running state of the robot in real time by using a monitoring camera to obtain a running state set; constructing a robot digital twin platform; acquiring an operation parameter random adjustment set and an operation state random adjustment set; synchronously recording panel display information of the robot, and acquiring a panel display information set; integrating the running parameter random adjustment set, the running state random adjustment set and the panel display information set to generate a remote diagnosis report of the robot; and sending the remote diagnosis report to the equipment side based on the robot digital twin platform to obtain a remote diagnosis result. This application has reached and has used 5g network platform as the basis, in time sends the robot remote control instruction, through the supplementary operation of cloud treater, improves the operating efficiency, fixes a position trouble node fast, and supplementary professional engineer carries out troubleshooting, reduces the fault handling time, in time carries out the technological effect of equipment maintenance to the robot.
Drawings
Fig. 1 is a schematic flow chart of a robot remote control instruction issuing method based on a 5g network according to the present application;
fig. 2 is a schematic flow chart of a robot remote control instruction issuing method based on a 5g network for generating a remote diagnosis report of a robot according to the present application;
fig. 3 is a schematic flow chart illustrating dynamic output of a remote diagnosis report according to a method for issuing a remote control command of a robot based on a 5g network according to the present application;
fig. 4 is a schematic structural diagram of a robot remote control instruction issuing system based on a 5g network according to the present application.
Description of reference numerals: the system comprises a variable modification unit 11, a parameter acquisition unit 12, an operation state acquisition unit 13, a digital platform construction unit 14, a random adjustment unit 15, a synchronous recording unit 16, a diagnosis report generation unit 17 and a diagnosis result acquisition unit 18.
Detailed Description
The application provides a robot remote control instruction issuing method and system based on a 5g network, the technical problem that equipment maintenance cannot be performed on a robot in time due to the fact that the robot remote control instruction is not sent timely is solved, the technical problem that the robot remote control instruction cannot be timely sent on the basis of the 5g network platform is solved, the robot remote control instruction is timely sent, operation is assisted through a cloud processor, operation efficiency is improved, fault nodes are quickly located, a professional engineer is assisted in troubleshooting, fault processing time is shortened, and the technical effect of performing equipment maintenance on the robot in time is achieved. This application can be applicable to industrial robot, cooperation robot, AGV transfer robot etc. like four-axis robot or six robots.
Example one
As shown in fig. 1, the present application provides a robot remote control instruction issuing method based on a 5g network, wherein the method includes:
step S100: modifying the variables of the running program of the robot based on a 5g network platform by combining a remote diagnosis calling instruction;
step S200: synchronously acquiring the operation parameters of the robot to obtain an operation parameter set;
step S300: monitoring the running state of the robot in real time by using a monitoring camera to obtain a running state set;
specifically, in the embodiment of the application, from the perspective of robot detection and maintenance, a remote control instruction is issued, the robot is synchronously subjected to fault detection, and the timeliness of operation, maintenance and synchronous monitoring is improved through high-speed 5g network connection.
Specifically, the field management personnel of the robot have limited cognition on the state parameters of the robot, cannot effectively monitor and early warn potential problems of the robot, and monitor various current and historical states of the robot in real time on the basis of a 5g network platform.
Further specifically, the remote diagnosis call instruction is a diagnosis start signal sent by a professional engineer at a remote debugging end, the remote diagnosis call instruction is synchronously sent to a machine end through a 5g network platform, a variable of an operation program of the robot is modified, the variable can be related index parameters such as working speed, operation acceleration, working load and repetition precision, the variable is modified through the remote diagnosis call instruction, operation parameter collection of the robot is synchronously performed, and an operation parameter set is obtained; the monitoring camera is a multi-channel high-definition video remote real-time monitoring device, and is used for monitoring the running state of the robot in real time, acquiring a running state set and providing a data basis for subsequent data processing.
Further specifically, once a factory robot fails, field debugging is required, and the robots are generally high in failure rate in various sizes, so that the debugging cost is high, the workload of professional operation and maintenance personnel is complicated, the working efficiency is low, a monitoring device (namely a monitoring camera) is additionally arranged on the field to monitor the running conditions of the robots and equipment, and data support is provided for assisting professional engineers at a remote debugging end to control field personnel to complete debugging.
Step S400: importing the operation parameter set and the operation state set into simulation software, performing index reduction setting, and building a robot digital twin platform;
specifically, using web streaming media based on ffmpeg (video editing conversion software) + jsmpeg (video and audio decoder) + node.js (scripting language of server language), based on the running state set, ffmpeg converts RTSP stream (file format) of a monitoring camera into MPEG stream (file format), and changes video coding from h.264 (video compression coding standard) to MPEG-1video (MPEG-1 is the first video and audio lossy compression standard established by MPEG organization), pushes the video stream to a cloud processor stably at high speed, receives the video stream pushed by ffmpeg under node.js running environment, provides a WebSocket port to the outside, imports the running parameter set, realizes multi-channel video efficient transmission based on WebSocket protocol, thereby ensuring low latency and continuity of video, Vue framework (progressive framework) is used as client base, node.js environment is used as cloud processor base, and (5) carrying out index initialization reduction setting, and building a basic framework of the robot digital twin platform.
Further specifically, the key technology of multipath real-time video transmission uses a web streaming media scheme and a WebSocket protocol based on ffmpeg + jsmppeg + nodejs, wherein ffmpeg provides a leading audio and video coding capability, jsmppeg and WebSocket ensure low delay and continuity of video streams, and in addition, the WebSocket protocol can reduce communication traffic, and can continuously transmit messages as long as WebSocket connection is established and is always kept.
Step S500: randomly changing the number of variables of the robot, and acquiring an operation parameter random adjustment set and an operation state random adjustment set through the remote diagnosis call instruction;
further, the number of variables of the robot is randomly changed, and the running parameter random adjustment set and the running state random adjustment set are obtained through the remote diagnosis call instruction, wherein the step S500 includes:
step S510: positioning variables of an operating program of the marking robot to generate variable marking information;
step S520: generating random numbers through a random algorithm, wherein the number of the random numbers is consistent with the number of the running program variables;
step S530: and modifying the variable mark information through a random number change rule and the random number, synchronously acquiring information, and acquiring an operation parameter random adjustment set and an operation state random adjustment set.
Specifically, positioning and marking a variable of an operating program of the robot through a variable marking signal, acquiring variable marking information after the positioning is finished, wherein the variable marking information is the variable information after the marking is finished, generating a group of random numbers through a random algorithm, the number of the random numbers is consistent with the number of the operating program variables, determining a random number change rule to be that the operating program variable corresponding to the random number of which the random number is an integral multiple of 3 needs to be modified, the random number change rule is a preset index parameter, exemplarily, the random number is [10, 15, 13, 6, 8], correspondingly, a second operating program variable and a fourth operating program variable need to be modified, the modification direction of the operating program variable is not unique, and can be modified in all stages, information acquisition is synchronously performed, and an operating parameter random adjustment set and an operating state random adjustment set are acquired, in the full-stage modification and adjustment process, the running parameter random adjustment set is used for synchronously acquiring running parameters of the full-stage modification and adjustment of the robot, and the running state random adjustment set is used for monitoring the running state of the full-stage modification and adjustment of the robot in real time by using a monitoring camera, so that data support is provided for the comprehensive evaluation of the state of the robot.
Step S600: synchronously recording panel display information of the robot, and acquiring a panel display information set;
step S700: integrating the running parameter random adjustment set, the running state random adjustment set and a panel display information set to generate a remote diagnosis report of the robot;
step S800: and sending the remote diagnosis report to an equipment end based on the robot digital twin platform to obtain a remote diagnosis result.
Further, the method further comprises:
step S910: judging whether the robot has a fault or not according to the remote diagnosis result;
step S920: if the fault occurs, the fault is positioned and marked through an abnormal alarm module, and a fault abnormal marking result is obtained;
step S930: and outputting the fault abnormity marking result to a remote debugging end through a cloud processor, and assisting a professional engineer of the remote debugging end to debug.
Specifically, the panel display information includes, but is not limited to, relevant parameter indexes such as motion mode, absolute accuracy, working coordinate, tool coordinate, payload, and the like, is specifically determined by specifically combining module function information of the robot production process, the panel display information of the robot is synchronously recorded in the process of acquiring an operation parameter random adjustment set and an operation state random adjustment set, the panel display information set is generated, the panel display information set, the operation parameter random adjustment set and the operation state random adjustment set are synchronous related information, the operation parameter random adjustment set, the operation state random adjustment set and the panel display information set are integrated to generate a remote diagnosis report of the robot, the remote diagnosis report is inserted based on the robot digital generation platform, and the remote diagnosis report is remotely connected with the equipment end of the robot through a cloud server, the robot digital twin platform and the implanted remote diagnosis report are transmitted, a remote diagnosis result is obtained after the verification and the determination of field workers at the equipment end of the robot are verified, the remote diagnosis result represents that a professional engineer at the remote debugging end needs to carry out auxiliary debugging, the remote diagnosis result comprises the robot digital twin platform and the implanted remote diagnosis report, the waste of equipment resources caused by data identification errors is avoided, technical support is provided for guaranteeing the effectiveness of the remote diagnosis result, and the stability of a robot remote control instruction issuing system is improved.
Specifically, based on the remote connection between a cloud processor and a remote debugging end, the remote diagnosis result is transmitted to the remote debugging end, and operation simulation is performed at the remote debugging end through the robot digital twin platform and the implanted remote diagnosis report to judge whether the robot breaks down; if the fault occurs, the fault is positioned and marked through the abnormal alarm module, a fault abnormal marking result is obtained, the fault abnormal marking result can be used for prominently marking the position coordinate of the fault and outputting the fault abnormal marking result to the remote debugging end, and a professional engineer at the auxiliary remote debugging end conducts debugging, so that the fault solving speed is increased while the operation cost of an enterprise is saved, the fault is quickly positioned, the professional engineer at the auxiliary remote debugging end conducts debugging, fault removal is conducted, the fault processing time is shortened, and the fault one-time restoration rate is increased.
Further, the operation parameter random adjustment set, the operation state random adjustment set and the panel display information set are integrated to generate a remote diagnosis report of the robot, and the step S700 includes:
step S710: acquiring data backup filing information;
step S720: through the data backup filing information, filing and sorting the programs corresponding to the operation parameter random adjustment set to obtain an operation program backup data packet;
step S730: and importing the running program backup data packet into a cloud processor, and integrating the running parameter random adjustment set, the running state random adjustment set and the panel display information set to generate a remote diagnosis report of the robot.
Specifically, in the adjusting process, in order to avoid covering original data, timely data backup and archiving processing is required, the data backup and archiving information comprises other related index information such as an operating program of the robot, a data backup signal is acquired before data adjustment, the data backup and archiving information is extracted through the data backup signal, data random adjustment is performed after data backup is finished, the data archiving signal is acquired after data adjustment is finished, in combination with the data backup and archiving information, a program corresponding to the operating parameter random adjustment set is archived and sorted, an operating program backup data packet is acquired, the operating program backup data packet comprises the operating program of the robot before data adjustment and the program corresponding to the operating parameter random adjustment set, and after the archiving and sorting is finished, the operating program backup data packet is remotely connected with the cloud processor through the equipment terminal, and importing the running program backup data packet into a cloud processor, integrating the running parameter random adjustment set, the running state random adjustment set and the panel display information set, generating a remote diagnosis report of the robot according to the integration result of the running program backup data packet, the running parameter random adjustment set, the running state random adjustment set and the panel display information set, providing technical support for ensuring the integrity of the remote diagnosis report, and providing data support for subsequent determination of a debugging scheme while avoiding data loss caused by data coverage.
Further, as shown in fig. 2, the running program backup data package is imported into a cloud processor, the running parameter random adjustment set, the running state random adjustment set and the panel display information set are integrated, and a remote diagnosis report of the robot is generated, where step S730 includes:
step S731: after the running program backup data packet is led into a cloud processor, a Node signal is obtained through the robot digital twin platform in a node.js environment;
step S732: integrating the running parameter random adjustment set, the running state random adjustment set and a panel display information set through a non-blocking I/O interface based on the Node signal to generate a digital twin model;
step S733: receiving, by the cloud processor, an operating state request of a client;
step S734: and generating a remote diagnosis report of the robot through the running state request and the digital twin model.
Further, as shown in fig. 3, the method further includes:
step S734-1: based on node.js environment, taking node.js as an http server to provide a web access interface, and receiving a running state request of a client through a cloud processor;
step S734-2: and pushing the remote diagnosis report to a client, and dynamically outputting the remote diagnosis report by the client based on an Echarts library.
Specifically, the Node signals are used for monitoring the operation of each part of the monitoring camera, the equipment end, the cloud server and the robot, and support is provided for realizing the communication of electrical information of each part, and realizing the coordination and coordination work.
Further specifically, in a node.js environment, after data integration and transmission are completed, a Node is notified, a Node signal is acquired through the digital twin platform of the robot, and the running parameter random adjustment set, the running state random adjustment set and the panel display information set are accessed and called through a non-blocking I/O interface, so that data iteration training is performed, and a digital twin model is generated; in the embodiment of the application, the remote connection can perform data communication transmission through a 5g network, receive an operation state request of a client based on the remote connection between the cloud processor and the client, and receive the operation state request to the cloud processor; and comparing the digital twin model output with the running state request to obtain a remote diagnosis report of the robot. Through Node signals, the access flow to resources is simplified, the development complexity is reduced, and the coordination working performance of each component is effectively improved.
Further specifically, in order to implement visual dynamic management of data, the dynamic data in the adjustment process is pushed to a service end operating based on a node.js environment, the node.js serves as an http server to provide a web access interface, and the service end receives a data request of a client and then receives an operation state request of the client through a cloud processor; and performing association mapping output on the dynamic data and the running state request to obtain an association mapping output result, pushing the association mapping output result and the remote diagnosis report to a client, and dynamically outputting the remote diagnosis report by the client based on an Echarts library to realize visual dynamic management of the dynamic data.
Further, based on the Node signal, the operation parameter random adjustment set, the operation state random adjustment set, and the panel display information set are integrated through a non-blocking I/O interface to generate a digital twin model, and step S732 includes:
step S732-1: based on the RBF network as a model, inputting the operation parameter random adjustment set and the operation state random adjustment set to the input end of the RBF network, and performing supervised training by taking a panel display information set as supervision data;
step S732-2: setting a node center and a node base width parameter of the RBF network;
step S732-3: inputting the running parameter random adjustment set, the running state random adjustment set and the panel display information set through an identification network at the front end of the RBF network;
step S732-4: and performing data iterative operation based on the node center and the node base width parameters, and generating a digital twin model after an error threshold value meets a preset error threshold value.
Specifically, iterative operation is carried out through a network model to ensure the precision of the digital twin model, an RBF network is taken as a model base, the operation parameter random adjustment set, the operation state random adjustment set and the panel display information set are grouped, the panel display information set, the operation parameter random adjustment set and the operation state random adjustment set are synchronous associated information, data grouping is carried out, each group of data comprises operation parameter random adjustment data, operation state random adjustment data and panel display information which are limited by the synchronous associated information, multiple groups of sequential operation parameter random adjustment data and operation state random adjustment data are input to the input end of the RBF network, and panel display information limited by the synchronous associated information is taken as supervision data to carry out supervision training; setting a node center and a node base width parameter of the RBF network through the output of an identification algorithm, wherein the node center and the node base width parameter both belong to parameter indexes in a Gaussian basis function of the RBF network; and based on the node center and the node base width parameters, inputting the operation parameter random adjustment set, the operation state random adjustment set and the panel display information set, performing data iterative operation, determining a digital twin model after the error threshold of the model output and the supervision data meets a preset error threshold, providing a model foundation for subsequent data processing, and providing data support for assisting a professional engineer at a remote debugging end to determine a modulation scheme by performing comparison analysis on the operation state request and the output of the digital twin model.
In summary, the method and system for issuing the robot remote control instruction based on the 5g network provided by the present application have the following technical effects:
because a 5 g-based network platform is adopted, and a remote diagnosis call instruction is combined, the variable of the running program of the robot is modified; synchronously acquiring the operation parameters of the robot to obtain an operation parameter set; monitoring the running state of the robot in real time by using a monitoring camera to obtain a running state set; constructing a robot digital twin platform; acquiring an operation parameter random adjustment set and an operation state random adjustment set; synchronously recording panel display information of the robot, and acquiring a panel display information set; integrating the running parameter random adjustment set, the running state random adjustment set and the panel display information set to generate a remote diagnosis report of the robot; and sending the remote diagnosis report to the equipment side based on the robot digital twin platform to obtain a remote diagnosis result. The application provides a robot remote control instruction issuing method and system based on a 5g network, and achieves the technical effects of timely sending a robot remote control instruction on the basis of a 5g network platform, improving the operation efficiency through cloud processor assisted operation, quickly positioning fault nodes, assisting professional engineers in troubleshooting, reducing fault processing time and timely carrying out equipment maintenance on a robot.
The remote diagnosis result is adopted to judge whether the robot has a fault; if the fault occurs, the fault is positioned and marked through an abnormal alarm module, and a fault abnormal marking result is obtained; through the cloud treater, export the unusual mark result of trouble to the remote debugging end, the professional engineer of supplementary remote debugging end debugs, improves the trouble solution speed when saving the operation cost of enterprise, carries out the quick location of trouble, and the professional engineer of supplementary remote debugging end debugs, carries out troubleshooting, reduces the fault handling time, improves the disposable repair rate of trouble.
After the operating program backup data packet is led into the cloud processor, in a node.js environment, a Node signal is obtained through a digital twin platform of a robot, and an operating parameter random adjustment set, an operating state random adjustment set and a panel display information set are integrated through a non-blocking I/O interface to generate a digital twin model; receiving an operation state request of a client through a cloud processor; and generating a remote diagnosis report of the robot through the running state request and the digital twin model. Through Node signals, the access flow of resources is simplified, the development complexity is reduced, and the coordination working performance of each component is effectively improved.
Example two
Based on the same inventive concept as the method for issuing the remote control command of the robot based on the 5g network in the foregoing embodiment, as shown in fig. 4, the present application provides a system for issuing the remote control command of the robot based on the 5g network, wherein the system includes:
the variable modification unit 11 is used for modifying the variable of the running program of the robot based on a 5g network platform by combining a remote diagnosis call instruction;
the parameter acquiring unit 12 is used for acquiring the operation parameters of the robot synchronously and acquiring an operation parameter set;
an operation state acquiring unit 13, where the operation state acquiring unit 13 is configured to use a monitoring camera to monitor an operation state of the robot in real time, and acquire an operation state set;
the digital platform building unit 14 is used for importing the operation parameter set and the operation state set into simulation software, performing index reduction setting and building a robot digital twin platform;
a random adjusting unit 15, wherein the random adjusting unit 15 is used for randomly changing the number of variables of the robot and acquiring a running parameter random adjusting set and a running state random adjusting set through the remote diagnosis calling instruction;
the synchronous recording unit 16 is used for synchronously recording panel display information of the robot and acquiring a panel display information set;
a diagnosis report generating unit 17, wherein the diagnosis report generating unit 17 is used for integrating the operation parameter random adjustment set, the operation state random adjustment set and the panel display information set to generate a remote diagnosis report of the robot;
and the diagnosis result acquisition unit 18 is used for sending the remote diagnosis report to an equipment side based on the digital twin platform of the robot to acquire a remote diagnosis result.
Further, the system comprises:
a variable tag generation unit for positioning a variable of an operation program of a tag robot and generating variable tag information;
the random number generating unit is used for generating random numbers through a random algorithm, wherein the number of the random numbers is consistent with the number of the running program variables;
and the information acquisition unit is used for modifying the variable mark information through a random number change rule and the random number, synchronously acquiring information and acquiring an operation parameter random adjustment set and an operation state random adjustment set.
Further, the system comprises:
the backup archiving unit is used for acquiring data backup archiving information;
the filing and sorting unit is used for filing and sorting the programs corresponding to the operation parameter random adjustment set through the data backup filing information to acquire an operation program backup data packet;
and the diagnosis report generation unit is used for importing the running program backup data packet into a cloud processor, integrating the running parameter random adjustment set, the running state random adjustment set and a panel display information set, and generating a remote diagnosis report of the robot.
Further, the system comprises:
a signal acquisition unit, configured to acquire a Node signal in a node.js environment through the robot digital twin platform after the operating program backup data packet is imported to a cloud processor;
the twin model generation unit is used for integrating the running parameter random adjustment set, the running state random adjustment set and the panel display information set through a non-blocking I/O interface based on the Node signal to generate a digital twin model;
a request receiving unit, configured to receive, by the cloud processor, an operation state request of a client;
a diagnostic report generating unit for generating a remote diagnostic report of the robot by the operation state request and the digital twin model.
Further, the system comprises:
the monitoring training unit is used for inputting the running parameter random adjustment set and the running state random adjustment set to the input end of the RBF network on the basis of taking the RBF network as a model, and performing monitoring training by taking the panel display information set as monitoring data;
the parameter setting unit is used for setting the node center and the node base width parameter of the RBF network;
the data input unit is used for inputting the running parameter random adjustment set, the running state random adjustment set and the panel display information set through an identification network at the front end of the RBF network;
and the iterative operation unit is used for performing data iterative operation based on the node center and the node base width parameter, and generating a digital twin model after an error threshold value meets a preset error threshold value.
Further, the system comprises:
the access interface determining unit is used for providing a web access interface by taking node.js as an http server based on node.js environment, and receiving an operation state request of a client through a cloud processor;
and the dynamic output unit is used for pushing the remote diagnosis report to a client, and the client dynamically outputs the remote diagnosis report based on an Echarts library.
Further, the system comprises:
the fault judgment unit is used for judging whether the robot has a fault or not according to the remote diagnosis result;
the positioning and marking unit is used for positioning and marking the fault through the abnormal alarm module if the fault occurs, and acquiring a fault abnormal marking result;
the debugging unit of supplementary remote debugging end, the debugging unit of supplementary remote debugging end is used for through the cloud treater, will the unusual mark result of trouble exports to the remote debugging end, and the professional engineer of supplementary remote debugging end debugs.
The specification and drawings are merely exemplary of the application and various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Such modifications and variations of the present application are within the scope of the claims of the present application and their equivalents, and it is intended that the present application include such modifications and variations.
Claims (8)
1. A robot remote control instruction issuing method based on a 5g network is characterized by comprising the following steps:
modifying the variables of the running program of the robot based on a 5g network platform by combining a remote diagnosis calling instruction;
synchronously acquiring the running parameters of the robot and acquiring a running parameter set;
monitoring the running state of the robot in real time by using a monitoring camera to obtain a running state set;
importing the operation parameter set and the operation state set into simulation software, performing index reduction setting, and building a robot digital twin platform;
randomly changing the number of variables of the robot, and acquiring an operation parameter random adjustment set and an operation state random adjustment set through the remote diagnosis call instruction;
synchronously recording panel display information of the robot, and acquiring a panel display information set;
integrating the running parameter random adjustment set, the running state random adjustment set and a panel display information set to generate a remote diagnosis report of the robot;
and sending the remote diagnosis report to an equipment end based on the robot digital twin platform to obtain a remote diagnosis result.
2. The method of claim 1, wherein the number of variables of the robot is randomly changed, and the random adjustment set of operating parameters and the random adjustment set of operating states are obtained through the remote diagnosis call instruction, the method comprising:
positioning variables of an operating program of the marking robot to generate variable marking information;
generating random numbers through a random algorithm, wherein the number of the random numbers is consistent with the number of the running program variables;
and modifying the variable mark information through a random number change rule and the random number, synchronously acquiring information, and acquiring an operation parameter random adjustment set and an operation state random adjustment set.
3. The method of claim 1, wherein the set of random adjustments to the operational parameters, the set of random adjustments to the operational status, and the set of panel display information are integrated to generate a remote diagnostic report for the robot, the method comprising:
acquiring data backup filing information;
through the data backup filing information, filing and sorting the programs corresponding to the operation parameter random adjustment set to obtain an operation program backup data packet;
and importing the running program backup data packet into a cloud processor, and integrating the running parameter random adjustment set, the running state random adjustment set and the panel display information set to generate a remote diagnosis report of the robot.
4. The method of claim 3, wherein the running program backup data package is imported into a cloud processor, and the running parameter random adjustment set, the running state random adjustment set and a panel display information set are integrated to generate a remote diagnosis report of the robot, and the method comprises:
after the running program backup data packet is imported into a cloud processor, a Node signal is obtained through the robot digital twin platform in a Node.
Integrating the running parameter random adjustment set, the running state random adjustment set and a panel display information set through a non-blocking I/O interface based on the Node signal to generate a digital twin model;
receiving, by the cloud processor, an operating state request of a client;
and generating a remote diagnosis report of the robot through the running state request and the digital twin model.
5. The method of claim 4, wherein the set of operational parameter random adjustments, the set of operational state random adjustments, and the set of panel display information are integrated via a non-blocking I/O interface based on the Node signals to generate a digital twin model, the method comprising:
based on the RBF network as a model, inputting the operation parameter random adjustment set and the operation state random adjustment set to the input end of the RBF network, and performing supervised training by taking a panel display information set as supervision data;
setting a node center and a node base width parameter of the RBF network;
inputting the running parameter random adjustment set, the running state random adjustment set and the panel display information set through an identification network at the front end of the RBF network;
and performing data iterative operation based on the node center and the node base width parameters, and generating a digital twin model after an error threshold value meets a preset error threshold value.
6. The method of claim 4, wherein the method further comprises:
based on node.js environment, node.js is used as http server to provide a web access interface, and a running state request of a client is received through a cloud processor;
and pushing the remote diagnosis report to a client, and dynamically outputting the remote diagnosis report by the client based on an Echarts library.
7. The method of claim 1, wherein the method further comprises:
judging whether the robot has a fault or not according to the remote diagnosis result;
if the fault occurs, the fault is positioned and marked through an abnormal alarm module, and a fault abnormal marking result is obtained;
and outputting the fault abnormity marking result to a remote debugging end through a cloud processor, and assisting a professional engineer of the remote debugging end to debug.
8. A robot remote control instruction issuing system based on a 5g network is characterized by comprising:
the variable modification unit is used for modifying the variable of the running program of the robot based on a 5g network platform by combining a remote diagnosis call instruction;
the robot comprises a parameter acquisition unit, a parameter acquisition unit and a parameter processing unit, wherein the parameter acquisition unit is used for synchronously acquiring the operation parameters of the robot and acquiring an operation parameter set;
the system comprises an operation state acquisition unit, a monitoring camera and a control unit, wherein the operation state acquisition unit is used for monitoring the operation state of the robot in real time by using the monitoring camera to acquire an operation state set;
the digital platform building unit is used for importing the operation parameter set and the operation state set into simulation software, carrying out index reduction setting and building a robot digital twin platform;
the random adjusting unit is used for randomly changing the number of variables of the robot and acquiring a running parameter random adjusting set and a running state random adjusting set through the remote diagnosis calling instruction;
the synchronous recording unit is used for synchronously recording panel display information of the robot and acquiring a panel display information set;
the diagnosis report generating unit is used for integrating the operation parameter random adjusting set, the operation state random adjusting set and the panel display information set to generate a remote diagnosis report of the robot;
and the diagnosis result acquisition unit is used for sending the remote diagnosis report to an equipment end based on the digital twin platform of the robot to acquire a remote diagnosis result.
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