CN113459110B - Control method and system of industrial mechanical arm based on 5G private network - Google Patents
Control method and system of industrial mechanical arm based on 5G private network Download PDFInfo
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Abstract
The invention discloses a control method and a control system of an industrial mechanical arm based on a 5G private network, which comprise a 5G private network system for providing a 5G communication environment; the cloud control monitoring server is used for issuing a production instruction and receiving desensitized state feedback data; the edge computing gateway is used for translating the production instruction into a control instruction which can be recognized by the industrial mechanical arm and cleaning the state feedback data; and the industrial mechanical arm is used for executing the production plan according to the control instruction. The invention applies the fifth generation mobile communication technology to the industrial production environment, builds a 5G private network system, optimizes the state rechecking comparison, the product processing data splitting storage, the state feedback data cleaning and the like aiming at the industrial application, and has the advantages of reliability, large throughput, low communication delay and good confidentiality.
Description
Technical Field
The invention relates to the technical field of 5G communication and industrial automation control, in particular to a control method and a control system of an industrial mechanical arm based on a 5G special network.
Background
With the continuous development of society and industrial technology, especially in the wave of 4.0 of the industry today, the traditional manufacturing industry is rapidly changing to the aspects of intellectualization and automation so as to meet the requirements of customers on high quality, rapid delivery and information traceability of products. Meanwhile, the labor cost is continuously increased, and the labor recruitment is difficult to realize in the labor-intensive enterprises; in addition, in particular industrial production situations, particularly in dirty, tiring, poor working environments, the young generation is generally reluctant to work in this area. Therefore, in the process of building a new intelligent factory and automatically upgrading and converting an existing factory, a large number of mechanical arms are needed to replace repetitive operations of personnel.
In practical applications, besides providing a solidified program instruction for the robot arm, it is often necessary to provide real-time and dynamic instruction control for the robot arm according to practical application scenarios. In order to accurately control data (such as critical physical and electrical parameters of working temperature, working voltage, working current, etc.) of all operation processes of the robot arm, sensors need to be deployed in a targeted manner. In addition, data fed back by the mechanical arm sensor and data fed back by image recognition devices such as an industrial camera and the like must be timely and accurately transmitted in place, so that a communication network deployed by the mechanical arm is required to have the characteristics of high throughput and low time delay.
However, currently, the robot arms of some factories are generally deployed at fixed positions, and then command control and data acquisition of the robot arms are realized through a wired network, such as an industrial ethernet, an optical fiber, an industrial control bus, or through a wireless network, such as a Wi-Fi, 4G mobile network in the factory. However, the deployment of the mechanical arm by using the wired network requires a large amount of construction and erection of the communication line and investment of hardware cost, and if the layout of the production line needs to be re-planned in the future, the re-erection of the physical communication line is involved, and the moving and deployment process is very inconvenient. The coverage mode of wireless networks such as Wi-Fi and 4G is adopted, and the like, and similarly, the defects exist, such as unstable communication caused by the fact that Wi-Fi is easily subjected to same frequency interference in a complex factory environment; the communication rate of 4G is not high and the communication delay is large, which cannot meet the industrial application.
In addition, in the industrial production process, in order to prevent leakage, the work of keeping secret on the relevant data of product production is also very important.
At present, the new generation mobile communication technology-5G not only solves the communication problem, but also provides a strong technical support for industrial application, and is continuously and rapidly integrated into various industries to enable industrial development.
Therefore, a method and a system for reliable, high throughput, low communication delay and good security are needed to solve the above problems of the industrial robot in the 5G private network environment.
Disclosure of Invention
The invention aims to provide a control method and a control system of an industrial mechanical arm based on a 5G private network.
In order to realize one of the purposes of the invention, the invention adopts the following technical scheme:
a control method of an industrial mechanical arm based on a 5G private network comprises the following steps:
a 5G private network system for providing a 5G communication environment;
the cloud control monitoring server is used for issuing a production instruction and receiving desensitized state feedback data;
the edge computing gateway is used for translating the production instruction into a control instruction which can be recognized by the industrial mechanical arm and cleaning the state feedback data;
the industrial mechanical arm is used for executing the production plan according to the control instruction;
the cloud control monitoring server is further used for dividing product processing data contained in a product drawing into a plurality of groups of spatial position parameters and detail parameters through a drawing separation algorithm, the spatial position parameters are stored in the cloud control monitoring server, the detail parameters are stored in the edge computing gateway, the spatial position parameters and the detail parameters in the same group are associated through unique identifiers without definite meanings, the spatial position parameters are issued to the edge computing gateway along with a production instruction, then the edge computing gateway synthesizes the spatial position parameters and the detail parameters, and generates a control instruction after coordinate conversion.
Furthermore, a 5G private network system is formed by the 5G core network and the BBU base station, the 5G private network system is connected with the cloud control monitoring server through the Internet in the north direction, and the industrial mechanical arm is connected with the 5G terminal through the RRU in the south direction.
Furthermore, the 5G private network system is directly connected with the edge computing gateway through a UPF user plane function module in the 5G core network.
Further, the edge computing gateway is further configured to perform state recheck on execution of the control instruction, the control instruction extracts a tolerance of a detail parameter to perform state recheck comparison with state feedback data received by the edge computing gateway, the industrial mechanical arm performs state recheck every time the industrial mechanical arm executes one control instruction, if the recheck is passed, the edge computing gateway sends the next control instruction to the industrial mechanical arm, and if the recheck is not passed, the edge computing gateway performs production interruption warning.
Furthermore, the drawing separation algorithm reads the predefined rule of the characteristic parameters in the product processing data, identifies the characteristic parameters in the product processing data, and divides the product processing data contained in the product drawing into a plurality of groups of spatial position parameters and detail parameters.
Further, the edge computing gateway cleans the state feedback data through a desensitization algorithm, and uploads the desensitized state feedback data to the cloud control monitoring server, wherein the desensitization algorithm is executed according to the following formula:
wherein Xn is the actual measurement value of the nth parameter in the state feedback data, Xn (max), Xn (min) are the preset maximum value and minimum value of the nth parameter, S [ n ] is an array for storing desensitized state feedback data, and alpha is a positive integer randomly generated for different industrial robots or factories.
In order to achieve the other purpose, the invention adopts the following technical scheme:
a control system of an industrial mechanical arm based on a 5G private network comprises:
the 5G private network system is used for providing a 5G communication environment;
the cloud control monitoring server is used for issuing a production instruction and receiving desensitized state feedback data;
the edge computing gateway is used for translating the production instruction into a control instruction which can be recognized by the industrial mechanical arm and cleaning the state feedback data;
the industrial mechanical arm is used for executing a production plan according to the control instruction;
the cloud control monitoring server is further used for dividing product processing data contained in a product drawing into a plurality of groups of spatial position parameters and detail parameters through a drawing separation algorithm, the spatial position parameters are stored in the cloud control monitoring server, the detail parameters are stored in the edge computing gateway, the spatial position parameters and the detail parameters in the same group are associated through unique identifiers without definite meanings, the spatial position parameters are issued to the edge computing gateway along with a production instruction, then the edge computing gateway synthesizes the spatial position parameters and the detail parameters, and generates a control instruction after coordinate conversion.
Furthermore, a 5G private network system is formed by the 5G core network and the BBU base station, the 5G private network system is connected with the cloud control monitoring server through the Internet in the north direction, and the industrial mechanical arm is connected with the 5G terminal through the RRU in the south direction.
Further, the edge computing gateway is further configured to perform state recheck on execution of the control instruction, the control instruction extracts a tolerance of a detail parameter to perform state recheck comparison with state feedback data received by the edge computing gateway, the industrial mechanical arm performs state recheck every time the industrial mechanical arm executes one control instruction, if the recheck is passed, the edge computing gateway sends the next control instruction to the industrial mechanical arm, and if the recheck is not passed, the edge computing gateway performs production interruption warning.
Furthermore, the drawing separation algorithm reads the predefined rule of the characteristic parameters in the product processing data, identifies the characteristic parameters in the product processing data, and divides the product processing data contained in the product drawing into a plurality of groups of spatial position parameters and detail parameters.
Compared with the prior art, the invention has the technical effects that:
the invention applies the fifth generation mobile communication technology to the industrial production environment, builds a 5G private network system, optimizes the state rechecking comparison, the product processing data splitting storage, the state feedback data cleaning and the like aiming at the industrial application, and has the advantages of reliability, large throughput, low communication delay and good confidentiality.
Drawings
FIG. 1 is a system architecture diagram of a control system of an industrial robot based on a 5G private network in an embodiment of the present invention;
FIG. 2 is a network topology diagram of a control system of an industrial robot based on a 5G private network in an embodiment of the invention;
fig. 3 is a data flow diagram of a control method of an industrial robot based on a 5G private network in an embodiment of the present invention;
fig. 4 is an on-line flowchart of an industrial robot of a control method for an industrial robot based on a 5G private network in an embodiment of the present invention;
fig. 5 is a flowchart of an online process of an edge computing gateway of a control method for an industrial robot based on a 5G private network in an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to specific embodiments shown in the drawings. These embodiments are not intended to limit the present invention, and structural, methodological, or functional changes made by those skilled in the art according to these embodiments are included in the scope of the present invention.
Referring to fig. 1, fig. 1 is a system architecture diagram of a control system of an industrial robot based on a 5G private network according to an embodiment of the present invention, showing a position and a connection relationship of the whole system deployment.
A control system of an industrial mechanical arm based on a 5G private network comprises:
the 5G private network system is used for providing a 5G communication environment;
the cloud control monitoring server is used for issuing a production instruction and receiving desensitized state feedback data;
the edge computing gateway is used for translating the production instruction into a control instruction which can be recognized by the industrial mechanical arm and cleaning the state feedback data;
the industrial mechanical arm is used for executing a production plan according to the control instruction;
the cloud control monitoring server is further used for dividing product processing data contained in a product drawing into a plurality of groups of spatial position parameters and detail parameters through a drawing separation algorithm, the spatial position parameters are stored in the cloud control monitoring server, the detail parameters are stored in the edge computing gateway, the spatial position parameters and the detail parameters in the same group are associated through unique identifiers without definite meanings, the spatial position parameters are issued to the edge computing gateway along with a production instruction, then the edge computing gateway synthesizes the spatial position parameters and the detail parameters, and generates a control instruction after coordinate conversion.
Furthermore, a 5G private network system is formed by the 5G core network and the BBU base station, the 5G private network system is connected with the cloud control monitoring server through the Internet in the north direction, and the industrial mechanical arm is connected with the 5G terminal through the RRU in the south direction.
The RRU is used for transmitting and receiving 5G private network signals, so that the 5G private network signals cover a certain area in the smart factory.
Preferably, the 5G terminal is a CPE (for providing a network channel for the industrial robot), and the industrial robot communicates with the RRU remote radio unit through the CPE and accesses the 5G private network system in a wireless manner.
Preferably, the BBU base station is expanded by using a HUB (HUB), and then a plurality of RRU remote units can be connected to expand the coverage of 5G private network signals in the smart factory.
Referring to fig. 2, fig. 2 is a network topology diagram of a control system of an industrial robot based on a 5G private network in an embodiment of the present invention, which shows a main network port connection manner of the system.
The cloud control monitoring server and the 5G private network system are connected to the Internet in an optical fiber mode; the 5G private network system adopts an optical switching mode to connect with an edge computing gateway in the south direction; the CPE is connected with a 5G private network system in a networking mode through a 5G wireless signal; the CPE and the core controller of the industrial mechanical arm are connected by Ethernet.
A control method of an industrial mechanical arm based on a 5G private network comprises the following steps:
a 5G private network system for providing a 5G communication environment;
the cloud control monitoring server is used for issuing a production instruction and receiving desensitized state feedback data;
the edge computing gateway is used for translating the production instruction into a control instruction which can be recognized by the industrial mechanical arm and cleaning the state feedback data;
the industrial mechanical arm is used for executing the production plan according to the control instruction;
the cloud control monitoring server is further used for reading a predefined rule of a characteristic parameter in product processing data through a drawing separation algorithm, identifying the characteristic parameter in the product processing data, dividing the product processing data contained in a product drawing into a plurality of groups of space position parameters (three-dimensional coordinates) and detail parameters (specific processing sizes), storing the space position parameters in the cloud control monitoring server, storing the detail parameters in an edge computing gateway, associating the space position parameters and the detail parameters in the same group with a unique identifier without definite meaning, synthesizing the space position parameters and the detail parameters through the unique identifier by the edge computing gateway after the space position parameters are issued to the edge computing gateway along with a production instruction, and generating a control instruction after coordinate conversion (coordinate conversion into the existing method).
It should be noted that the product processing data included in a complete product drawing includes a plurality of characteristic parameters and predefined rules of the characteristic parameters. The characteristic parameters include a spatial position parameter and a detail parameter. The file for storing the predefined rules of the characteristic parameters records which characteristic parameters are spatial position parameters, which characteristic parameters are detail parameters, and a certain spatial position parameter and which detail parameters have a corresponding relationship. After a drawing separation algorithm running in the cloud control monitoring server reads a predefined rule of the characteristic parameters, the product processing data can be divided into two types of data, namely space position parameters and detail parameters.
Meanwhile, after the product processing data is split into two types of data, namely spatial position parameters and detail parameters, the spatial position parameters and the detail parameters with corresponding relations (in the same group) are associated by unique identifiers without clear meanings, so that the corresponding relations of the spatial position parameters and the detail parameters in the same group are ensured to be unchanged in the data splitting and synthesizing processes.
The unique identifier without explicit meaning is generated by a random algorithm in the cloud control monitoring server. And the method is used for identifying the corresponding relation between the spatial position parameters and the detail parameters. The randomly generated identifier has no definite meaning, namely specific numerical values of the space position parameter and the detail parameter cannot be calculated from the identifier alone, so that the high confidentiality of the split data is ensured.
Taking an example of machining a hole with a radius of r =10mm on a product by taking a point a as a center, wherein product machining data included in a product drawing includes x =30mm, y =20mm, z =0mm, and r =10 mm; the predefined rule of the characteristic parameter states that "x", "y", "z" are spatial position parameters of the point a on the product, "r" is a detail parameter (of course, in other examples, the detail parameter may include richer contents, such as depth, direction angle of the hole, etc., and the detail parameter only includes a radius as an example), and the detail parameter r =10mm is associated with the spatial position parameter x, y, z = (30mm, 20mm, 0mm), and after the parameter is split, the parameters are separated into two types of data:
three-dimensional coordinates x, y, z = (30mm, 20mm, 0mm) of point A
Point A as the center of circle, radius r =10mm
The spatial position parameter is associated with the detail parameter by a unique identifier uuid = p001 without clear meaning, the detail parameter is held by a factory, and the cloud control monitoring server stores the spatial position parameter.
The split spatial position parameters and detail parameters are expressed in a data mode as follows:
spatial position parameter
{
“uuid”：“p001”，
“x”：“30”，
“y”：“20”，
“z”：“0”
}
Detail parameter
{
“uuid”：“p001”，
“r”：“10”
}
More vividly, expressed in tabular form as:
| type of parameter | Unique identifier uuid | Parameters bound to a unique identifier |
| Spatial position parameter | p001 | x=30；y=20；z=0 |
| Detail parameter | p001 | r=10 |
By splitting and storing the product processing data respectively and only performing association synthesis through the unique identifier during production, the complete product processing data can be effectively prevented from leaking, and the method is beneficial to improving the confidentiality. For example, even if someone acquires the spatial location parameters stored in the cloud control monitoring server, counterfeit production of the product cannot be performed due to lack of detail parameters.
Furthermore, a 5G private network system is formed by the 5G core network and the BBU base station, the 5G private network system is connected with the cloud control monitoring server through the Internet in the north direction, and is connected with the industrial mechanical arm through the RRU and the CPE in the south direction.
Furthermore, the 5G private network system is directly connected with the edge computing gateway through a UPF user plane function module in the 5G core network.
The UPF user plane function module is an existing function module in a 5G core network of the 5G communication system. A5G core network of the 5G private network system is divided into a UPF user plane functional module and other modules, and the UPF user plane functional module is directly connected with the edge computing gateway, so that data communication between a core controller of the industrial mechanical arm and the edge computing gateway does not need to be carried out on the 5G core network realized by software, and the data communication can be directly transmitted through bottom layer exchange. The bidirectional average time delay between the core controller of the industrial mechanical arm and the edge computing gateway can be within 10ms, and the action synchronism and consistency of the industrial mechanical arm are greatly improved.
Further, the edge computing gateway is further configured to perform status review on execution of the control command, where the control command extracts a tolerance of a detail parameter (the detail parameter in the product processing data usually has a tolerance that is an acceptable processing error range of the workpiece, for example, the detail parameter in the example of the processing hole location may be recorded as r =10 ± 0.1 mm), and performs status review comparison with status feedback data received by the edge computing gateway, where the industrial robot performs status review each time the industrial robot executes one control command, and if the review passes, the edge computing gateway sends the next control command to the industrial robot, and if the review does not pass, a production interruption alarm is performed, and the workpiece is also rejected from a processing sequence.
For example, the industrial robot executes a control command to drill a workpiece, the detail parameter of the drill is r =10mm, the tolerance of the detail parameter extracted by the control command is ± 0.1mm, the industrial robot sends state feedback data to the edge computing gateway after executing the control command of the drill, and if r in the state feedback data is between 9.9 mm and 10.1mm, the recheck is passed, otherwise, the recheck is not passed.
Compared with the traditional mode of detection after production in the manufacturing industry (detection after all production procedures of the product are finished), the method has the advantages that the problem links in the production process can be found more timely by performing state recheck (segmented processing and segmented detection) in the production process, correction is conveniently and timely performed, the rejection rate in the whole production process is greatly reduced, and the production cost is saved.
Further, the edge computing gateway cleans the state feedback data through a desensitization algorithm, and uploads the desensitized state feedback data to the cloud control monitoring server, wherein the desensitization algorithm is executed according to the following formula:
wherein Xn is the actual measurement value of the nth parameter in the state feedback data, Xn (max), Xn (min) are the preset maximum value and minimum value of the nth parameter, S [ n ] is an array for storing desensitized state feedback data, and alpha is a positive integer randomly generated for different industrial robots or factories.
The data desensitization factor alpha is a positive integer randomly generated aiming at different industrial mechanical arms or factories, alpha specific numerical values corresponding to the industrial mechanical arms or factories are mastered by a small number of managers and are strictly kept secret, and the specific numerical values of Xn cannot be reversely deduced through S [ n ] on the premise of not knowing the alpha specific numerical values.
It should be noted that the state feedback data Xn includes not only the actual measured product machining dimension parameters during the machining process, but also the environment and electrical parameters of the industrial robot arm during the workpiece machining, and if the feedback data are not processed, the feedback data are directly uploaded to the cloud control monitoring server, and once the feedback data leak, the detailed parameters and the machining process of the product can be reversely deduced.
The state feedback data is cleaned through a desensitization algorithm, the real data value can be fuzzified, parts, which may reveal detailed parameters and a processing technology, in the state feedback data are erased and are excluded from production archived data, so that the aim of desensitization is fulfilled, the desensitized state feedback data only can display the fluctuation of the original state feedback data, actual detailed parameters and the processing technology of a product cannot be obtained through backward estimation, and the confidentiality is further improved.
Referring to fig. 3, fig. 3 is a data flow diagram of a control method for an industrial robot based on a 5G private network in an embodiment of the present invention, which shows an execution process of the control method.
The data flow direction of the control method of the invention is divided into 7 stages:
stage 1:
the edge computing gateway accesses the internet through a 5G private network system, accesses a preset cloud control monitoring server, performs online registration, and reports the integrity of each level of equipment and the integrity of various consumables of the current factory. And after the production line planning adjustment is completed, the edge computing gateway reports that the preparation in the early production stage is completed and waits for issuing a production plan.
And (2) stage:
and the cloud control monitoring server issues a production instruction to the edge computing gateway according to a production plan set by a user, and data encryption processing is carried out in the middle through SSL. Ensuring that the production instructions come from an authorized server and the middlings are not tampered.
And (3) stage:
the edge computing gateway analyzes and translates the production instruction of the cloud control monitoring server, distributes the detail flow needing to be processed of the industrial mechanical arm corresponding to the processing station, and summarizes the detail flow into the control instruction which can be executed by the industrial mechanical arm.
And (4) stage:
after finishing the instruction translation and aggregation, the edge computing gateway sends the control instruction to the industrial mechanical arm of each station through the 5G private network system and the 5G CPE.
And (5) stage:
and the industrial mechanical arm receives the control command and sequentially processes. Meanwhile, in the processing process, the electric state data of the industrial mechanical arm and the processing size data of the material part sensed by the sensor are collected in real time, and all state feedback data are transmitted to the edge computing gateway through the 5G CPE and the 5G private network system.
And 6:
and the edge computing gateway receives the state feedback data, performs state recheck according to the detail parameter tolerance extracted by the control instruction, and continuously sends the next material processing control instruction to the industrial mechanical arm if the recheck is passed. If the rechecking is not passed, the key data are marked and translated and then fed back to a preset cloud control monitoring server through a 5G private network system through the Internet (data generated in the production process are stored, data tracing can be achieved), and production interruption state warning is carried out.
And (7) stage:
and the edge computing gateway synthesizes the state feedback data of the whole production line for cleaning, and uploads the desensitization data to the cloud control monitoring server.
Referring to fig. 4, fig. 4 is a flowchart of an industrial robot on-line of a control method for an industrial robot based on a 5G private network according to an embodiment of the present invention, showing an on-line process of the industrial robot.
The on-line process of the industrial mechanical arm comprises the following steps:
1. electrifying and self-checking;
2. acquiring equipment IP and related server IP and access configuration through DHCP;
3. detecting the availability of IP and server configuration;
4. if the IP and the server configuration are available, initializing and connecting to the edge computing gateway, and if the IP and the server configuration are unavailable, returning and executing to obtain the equipment IP and the access configuration of the relevant server through the DHCP;
5. if the connection of the gateway is successful, finishing the interactive authentication process of the edge computing gateway and the industrial mechanical arm, reporting the equipment capability and the current working condition of the industrial mechanical arm, and if the connection of the gateway is unsuccessful, returning to execute initialization to connect the edge computing gateway;
6. after the gateway is successfully connected, waiting for the edge computing gateway to issue a control instruction;
7. if the control instruction is received, executing the production operation step, and if the control instruction is not received, continuing waiting for the edge computing gateway to issue the control instruction;
8. after the production operation step is executed, uploading sensor data in the production process;
9. and then continuously waiting for the next control instruction issued by the edge computing gateway.
Referring to fig. 5, fig. 5 is a flowchart illustrating an online process of an edge computing gateway in the method for controlling an industrial robot based on a 5G private network according to the embodiment of the present invention.
The online process of the edge computing gateway is as follows:
1. electrifying and self-checking;
2. reading access configuration of a cloud control monitoring server;
3. connecting the cloud control monitoring server according to the pre-configuration trial, performing interactive authentication if the connection is successful, returning to execute the access configuration of the cloud control monitoring server if the connection is unsuccessful, and trying to connect again;
4. if the interactive authentication is successful, the periodic heartbeat packet is connected with the cloud control monitoring server, and if the interactive authentication is unsuccessful, the execution of the interactive authentication is returned;
5. waiting for a cloud production instruction;
6. if the production instruction is received, performing instruction translation and flow archiving according to the local production line deployment configuration, and if the production instruction is not received, continuing waiting;
7. issuing a control instruction of the industrial mechanical arm, and waiting for state feedback data returned by the industrial mechanical arm;
8. if the state feedback data is received, performing state rechecking comparison, and if the state feedback data is not received, performing alarm;
9. if the rechecking is passed, entering the next production process, if the rechecking is not qualified, giving an alarm, and waiting for manual checking of the workpiece.
The edge computing gateway is also used to manage all industrial robots that have successfully registered. The industrial mechanical arms are registered on the edge computing gateway through the online process, and the edge gateway divides all the industrial mechanical arms into a plurality of categories such as spraying category, drilling category, cutting category and welding category according to model function parameters provided by the industrial mechanical arms. The edge computing gateway arranges the production and processing flow according to the production plan and the process requirements of the processing workpieces, generates a mechanical production team group, and issues instructions to various industrial mechanical arms to enter different work stations for standby waiting.
Compared with the prior art, the invention has the technical effects that:
the invention applies the fifth generation mobile communication technology to the industrial production environment, builds a 5G private network system, optimizes the state rechecking comparison, the product processing data splitting storage, the state feedback data cleaning and the like aiming at the industrial application, and has the advantages of reliability, large throughput, low communication delay and good confidentiality.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may be modified or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A control method of an industrial mechanical arm based on a 5G private network is characterized by comprising the following steps:
providing a 5G communication environment through a 5G private network system;
the cloud control monitoring server divides product processing data contained in a product drawing into a plurality of groups of spatial position parameters and detail parameters through a drawing separation algorithm, the spatial position parameters are stored in the cloud control monitoring server, the detail parameters are stored in the edge computing gateway, and the spatial position parameters and the detail parameters in the same group are associated by unique identifiers without clear meanings;
the cloud control monitoring server issues a production instruction, the spatial position parameter is issued to the edge computing gateway along with the production instruction, the edge computing gateway synthesizes the spatial position parameter and the detail parameter, and a control instruction which can be identified by the industrial mechanical arm is generated after coordinate conversion;
the industrial mechanical arm executes a production plan according to the control instruction and sends state feedback data to the edge computing gateway;
and the edge computing gateway cleans the state feedback data and sends the desensitized state feedback data to the cloud control monitoring server.
2. The method for controlling the industrial robot based on the 5G private network according to claim 1, wherein a 5G private network system is formed by the 5G core network and the BBU base station, the 5G private network system is connected with the cloud control monitoring server through the Internet in the north direction, and is connected with the industrial robot through the RRU and the 5G terminal in the south direction.
3. The method for controlling an industrial robot based on a 5G private network according to claim 2, wherein the 5G private network system is directly connected to the edge computing gateway through a UPF user plane function module in a 5G core network.
4. The method according to claim 1, wherein the edge computing gateway is further configured to perform state recheck on execution of the control instruction, the control instruction extracts a tolerance of a detail parameter to perform state recheck comparison with state feedback data received by the edge computing gateway, the industrial robot performs state recheck every time the industrial robot executes one control instruction, if the recheck is passed, the edge computing gateway sends a next control instruction to the industrial robot, and if the recheck is not passed, the industrial robot performs production interruption warning.
5. The method as claimed in claim 1, wherein the drawing separation algorithm reads predefined rules of characteristic parameters in the product processing data, identifies the characteristic parameters in the product processing data, and divides the product processing data contained in the product drawing into a plurality of groups of spatial position parameters and detail parameters.
6. The control method of the industrial mechanical arm based on the 5G private network is characterized in that the edge computing gateway cleans state feedback data through a desensitization algorithm, uploads the desensitized state feedback data to the cloud control monitoring server, and the desensitization algorithm is executed according to the following formula:
wherein Xn is the actual measurement value of the nth parameter in the state feedback data, Xn (max), Xn (min) are the preset maximum value and minimum value of the nth parameter, S [ n ] is an array for storing desensitized state feedback data, and alpha is a positive integer randomly generated for different industrial robots or factories.
7. The utility model provides a control system of industrial robot arm based on 5G private network which characterized in that includes:
the 5G private network system is used for providing a 5G communication environment;
the cloud control monitoring server is used for issuing a production instruction and receiving desensitized state feedback data;
the edge computing gateway is used for translating the production instruction into a control instruction which can be recognized by the industrial mechanical arm and cleaning the state feedback data;
the industrial mechanical arm is used for executing a production plan according to the control instruction;
the cloud control monitoring server is further used for dividing product processing data contained in a product drawing into a plurality of groups of spatial position parameters and detail parameters through a drawing separation algorithm, the spatial position parameters are stored in the cloud control monitoring server, the detail parameters are stored in the edge computing gateway, the spatial position parameters and the detail parameters in the same group are associated through unique identifiers without definite meanings, the spatial position parameters are issued to the edge computing gateway along with a production instruction, then the edge computing gateway synthesizes the spatial position parameters and the detail parameters, and generates a control instruction after coordinate conversion.
8. The control system of the industrial robot based on the 5G private network according to claim 7, wherein the 5G private network system is formed by a 5G core network and a BBU base station, the 5G private network system is north-connected with the cloud control monitoring server through the Internet, and south-connected with the industrial robot through the RRU and the 5G terminal.
9. The system according to claim 7, wherein the edge computing gateway is further configured to perform status recheck on execution of the control command, the control command extracts a tolerance of a detail parameter to perform status recheck comparison with status feedback data received by the edge computing gateway, the industrial robot performs status recheck every time the industrial robot executes one control command, if the recheck is passed, the edge computing gateway sends the next control command to the industrial robot, and if the recheck is not passed, the industrial robot performs production interruption warning.
10. The control system of the industrial robot arm based on the 5G private network as claimed in claim 7, wherein the drawing separation algorithm reads the predefined rule of the characteristic parameter in the product processing data, identifies the characteristic parameter in the product processing data, and divides the product processing data contained in the product drawing into a plurality of groups of spatial position parameters and detail parameters.
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