CN115980062A - Industrial production line whole-process vision inspection method based on 5G - Google Patents
Industrial production line whole-process vision inspection method based on 5G Download PDFInfo
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Abstract
The invention discloses a 5G-based industrial production line overall process vision inspection method, which comprises the following steps: constructing an industrial production line AR map model by utilizing an AR function and an object AR map; performing set calculation on the distribution condition of the staff and the distribution condition of the industrial equipment in the industrial production line area displayed on the AR map by using a distribution statistical model; when equipment fails, acquiring real-time dynamic data of an equipment detection sensor, and positioning the specific position of the failed equipment on an AR map by using a ZigBee automatic positioning model; automatically analyzing various operation data around the fault equipment site, and displaying on an AR map; the shortest visual inspection route is calculated and displayed on the AR map, and the inspection route map can be sent to a mobile equipment end of a manager through a wireless network, so that the manager can conveniently check the inspection route map; the method effectively solves the problem that images and data in the machine vision system in the traditional mode are difficult to coordinate and unify, and accelerates the inspection speed of an industrial production line.
Description
Technical Field
The invention relates to the field of industrial vision, in particular to a 5G-based industrial production line whole-process vision inspection method.
Background
The machine vision system is an important application system which is very common in the industrial manufacturing environment, can replace human eyes to carry out measurement and discrimination, can greatly improve the measurement precision, the discrimination accuracy and the discrimination speed, increase complicated and severe application occasions, can realize the functions of micro-size measurement, high-speed industrial on-line detection and the like, can furthest improve the efficiency of modern industrial automatic production, and is a necessary component part in each stage of production and manufacturing.
The task of the traditional mode machine vision system is mainly simple image acquisition, image analysis and judgment action making, with the development of a new round of information technologies such as the Internet of things, big data, cloud computing, artificial intelligence and 5G communication and the like, the global industrial revolution starts to put up schedules, industrial transformation starts to enter the essential stage of intelligent manufacturing, and the traditional machine vision system gradually exposes the bottleneck problems of independent work of a single machine, high cost, formation of a data island, complex line maintenance and software upgrading, short wired transmission distance, long deployment time and the like.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a 5G-based industrial production line whole-process vision inspection method.
The technical scheme adopted by the invention is that the method comprises the following steps:
step S1: constructing an industrial production line AR map model by utilizing an AR function and an object AR map;
step S2: performing set calculation on the distribution condition of the staff and the distribution condition of the industrial equipment in the industrial production line area displayed on the AR map by using a distribution statistical model;
and step S3: when equipment fails, acquiring real-time dynamic data of an equipment detection sensor, and positioning the specific position of the failed equipment on an AR map by using a ZigBee automatic positioning model;
and step S4: automatically analyzing each item of operation data around the fault equipment site, and displaying on an AR map, wherein the method comprises the following steps:
step A1: establishing a multi-source information aggregation function to calculate the running condition of a peripheral production line of the fault equipment, the information of operators and the distance between the operator and the fault equipment;
step A2: displaying the field real-time condition of the industrial production line by using a visual imaging function, wherein the field real-time condition comprises equipment running condition, yield, production speed and production environment information;
step A3: a manager analyzes and makes a decision to make a visual inspection scheme and calculates the feasibility of the visual inspection;
step S5: and calculating the shortest visual inspection route, displaying the shortest visual inspection route on the AR map, and sending the inspection route map to a mobile equipment terminal of a manager through a wireless network so as to facilitate the manager to check the inspection route map.
Further, the industrial production line AR map model is constructed by utilizing the AR function and the object AR map, and the expression is as follows:
wherein,
representing an AR map image of an industrial production line>
A matrix of AR coefficients representing different objects on an industrial line, <' > based on the AR coefficients>
AR function representing an object>
Represents a color weight of the object>
Represents a color composition function of the object>
Representing an integration operation on a color>
AR scale matrix representing different objects>
Representing an integral operation of the running time of the object.
Further, the distribution statistical model is used for performing set calculation on the distribution condition of the staff and the distribution condition of the industrial equipment in the area of the industrial production line displayed on the AR map, and the expression is as follows:
wherein,
represents a statistical function of the distribution on an industrial production line,. According to the present invention>
Indicating the distribution of the employee's location,
function of the number of persons representing employees at different posts, <' >>
Represents a distribution function of industrial plant locations>
Representing a function of the number of devices on different industrial processes.
Further, the ZigBee automatic positioning model has an expression as follows:
wherein N represents the coordinate of the fault equipment, K represents the positioning coefficient of ZigBee, and F C A real-time data classification function representing equipment detection sensors, m represents the number of faulty equipment, theta represents a fault factor of the equipment, b represents a standard operating time of the equipment,
indicating the length of time the device has been operating.
Further, the expression of the multi-source information set function is as follows:
wherein,
a multi-source information aggregation function is represented,Irepresents a multi-source information aggregation coefficient, n represents individual monitoring data, U represents the total amount of monitoring data, and->
A classification function representing information of a peripheral production line and an operator, based on the information of the peripheral production line and the operator>
Represents a threshold value set of distances between a peripheral production line, an operator and fault equipment, and>
dynamic mean value representing distance between peripheral production line, operator and faulty equipment>
Representing a distance data error function;
further, the visual imaging function has the expression:
wherein,
represents a visual imaging function, <' > based on the image data>
Data sets which represent the operating conditions, the production speed and the production environment of the apparatus are evaluated>
Analysis of presentation imagesDegree, or>
Represents a data error set representing information on the operation, production speed, production environment of the apparatus, and/or>
Which is indicative of an error factor of the data,
further, the feasibility of the vision inspection is calculated, and the expression is as follows:
wherein,
represents the feasibility degree of the vision inspection and combines the functions of the vision inspection and the illumination inspection>
Represents the minimum visual field range in the visual inspection process and is matched>
Represents the maximum visual field range during visual inspection, e represents a natural constant and is based on the principle of the real-time detection of the real-time vision inspection, and>
an angle function representing the visual inspection.
Further, the shortest routing inspection path of the vision routing inspection is calculated, and the expression is as follows:
wherein,
represents the shortest routing inspection path of vision inspection and is judged>
Represents the visual inspection time and is judged>
Representing the total length of the production line, <' >>
Indicates a length not being visually inspected, and>
indicating the distance error of the visual inspection.
Has the beneficial effects that:
the invention provides a 5G-based industrial production line whole-process visual inspection method, which is used for visually inspecting information such as equipment, personnel and the like on an industrial production line through an AR (augmented reality) technology by using an algorithm fused by multiple models, effectively solves the problem that images and data in a machine vision system in a traditional mode are difficult to coordinate and unify, reduces the workload of managers, and accelerates the inspection speed of the industrial production line.
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FIG. 1 is a first flowchart of the method of the present invention;
FIG. 2 is a second flowchart of the method of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments can be combined with each other without conflict, and the present application will be further described in detail with reference to the drawings and specific embodiments.
As shown in FIG. 1, the 5G-based industrial production line whole-process vision inspection method comprises the following steps:
an OM-A6X base station of the data dynamic environment monitoring all-in-one machine of the industrial production line processes data,
the OM-A6X base station is a high-performance integrated monitoring host suitable for monitoring various machine room power environments. The system is based on a 32-bit Feichka industrial ARM embedded chip with low power consumption, a Linux operating system, a large-capacity storage and a convenient embedded WEB service, can complete the working states and various operating parameters of equipment such as commercial power, power distribution, UPS, a storage battery, temperature and humidity, an air conditioner, water leakage, a fresh air machine, smoke detection, fire protection, lightning protection, infrared, entrance guard, video (image acquisition), a server, a router, a switch and the like without depending on a network and upper computer software, performs centralized monitoring management, and gives an alarm through short messages, telephones, mails, sound and light, weChat and the like.
Under the abnormal conditions of theft, fire, power failure and the like, the operations of key data acquisition, field image acquisition, alarm information release, remote control and the like are completed. The host provides working power supply for various sensors, a built-in backup power supply can be used for independently providing power for the host and the sensors in a power failure state, an IE browser can be directly used for monitoring and management, a complete TCP/IP function is built in the system, and the double-network-port design can realize flexible networking or cross-platform seamless integration.
Meanwhile, the machine room monitoring centralized management cloud platform is matched to manage the AR, is designed aiming at the centralized supervision requirements of the power environments of the machine rooms in a plurality of machine rooms and different regions, utilizes communication technologies such as a private network, a public network and wireless, is flexibly deployed according to the actual requirements of user management application, and achieves hierarchical comprehensive monitoring management.
The functional characteristics of the platform include: the centralized monitoring of thousands of machine rooms is supported, and the cross-region centralized monitoring management is realized for a plurality of machine rooms distributed in various places; the system has the function of monitoring the regional diagram on line or off line, and intuitively masters the monitoring dynamics of a plurality of machine rooms; the management of the embedded 3D machine room graph is supported, and the running state of the machine room monitoring equipment is browsed visually; the IE mode is completely browsed and managed, and a B/S framework is adopted, so that the maintenance and the upgrade are convenient; the log management system has a complete log management query function and provides a perfect management system; the system has the asset management functions, including asset registration, asset query, asset backup, maintenance record and the like; the method supports various modes of alarming, including short message alarming, telephone alarming, weChat alarming, webpage alarming, mail alarming, app alarming and the like; support multi-user management authority function; a PUE statistical function of machine room energy consumption analysis is supported; supporting the display of a curve trend chart and a bar chart of the monitoring equipment; and embedding a powerful report output system. And the flexible setting of the multi-level alarm levels supports secondary development and OEM.
Step S1: constructing an industrial production line AR map model by utilizing an AR function and an object AR map;
the industrial production line AR map model is related to an industrial production line AR map image, an AR coefficient matrix of different objects on an industrial production line, an AR function of the object, color weight of the object, a color composition function of the object, integral operation on color, an AR scaling matrix of different objects and integral operation on the running time of the object.
Step S2: performing set calculation on the distribution condition of the staff and the distribution condition of the industrial equipment in the industrial production line area displayed on the AR map by using a distribution statistical model;
the distribution statistical model is related to distribution statistical functions on an industrial production line, distribution conditions of staff positions, people number functions of staff at different posts, industrial equipment position distribution functions and equipment quantity functions on different industrial processes.
And step S3: when equipment fails, acquiring real-time dynamic data of an equipment detection sensor, and positioning the specific position of the failed equipment on an AR map by using a ZigBee automatic positioning model;
the ZigBee automatic positioning model is related to the coordinates of fault equipment, the positioning coefficient of ZigBee, a real-time data classification function of equipment detection sensors, the number of fault equipment, fault factors of the equipment, the standard operation time of the equipment and the operated time of the equipment.
And step S4: various operation data around the fault equipment site are automatically analyzed and displayed on an AR map, as shown in FIG. 2, including:
step A1: establishing a multi-source information aggregation function to calculate the running condition of a peripheral production line of the fault equipment, the information of operators and the distance between the peripheral production line of the fault equipment and the fault equipment;
the system comprises a multi-source information set function, a multi-source information set coefficient, single monitoring data, monitored data total, a classification function of information of a peripheral production line and an operator, a threshold value set of distances between the peripheral production line, the operator and a fault device, a dynamic average value of the distances between the peripheral production line, the operator and the fault device, and a distance data error function.
Step A2: displaying the on-site real-time condition of the industrial production line by using a visual imaging function, wherein the on-site real-time condition comprises equipment running condition, yield, production speed and production environment information;
the visual imaging function is related to a data set of equipment operation condition, yield, production speed and production environment information, resolution of images, a data error set representing the equipment operation condition, the yield, the production speed and the production environment information, and a data error factor.
Step A3: a manager analyzes and makes a decision to make a visual inspection scheme and calculates the feasibility of the visual inspection;
the feasibility degree of the vision inspection is calculated and is related to the feasibility degree of the vision inspection, the minimum visual field range during the vision inspection, the maximum visual field range during the vision inspection, a natural constant and an angle function of the vision inspection.
Step S5: and calculating the shortest visual inspection route, displaying the shortest visual inspection route on the AR map, and sending the inspection route map to a mobile equipment terminal of a manager through a wireless network so as to facilitate the manager to check the inspection route map.
The shortest route for visual inspection and the shortest route for visual inspection have the visual inspection time,
indicating the total length of the production line, the length not being inspected by vision, the distance error of vision inspection.
An industrial production line AR map model is constructed by utilizing an AR function and an object AR map, and the expression is as follows:
wherein,
representing an AR map image of an industrial production line>
A matrix of AR coefficients representing different objects on the industrial line,
AR function representing an object>
Represents a color weight of the object>
Represents a color composition function of the object>
Represents an integration operation on the color, is asserted>
AR scale matrix representing different objects>
Representing an integral operation of the running time of the object. />
Utilizing a distribution statistical model to perform set calculation on the distribution condition of the staff and the industrial equipment in the industrial production line area displayed on the AR map, wherein the expression is as follows:
wherein,
represents a statistical function of the distribution on an industrial production line,. According to the present invention>
Indicating the distribution of the employee's location,
function of the number of persons representing employees at different posts, <' >>
Represents a function of the distribution of the position of the industrial installation, <' > or>
Representing a function of the number of devices on different industrial processes.
ZigBee automatic positioning model, the expression is:
wherein N represents the coordinate of the fault equipment, K represents the positioning coefficient of ZigBee, and F C A real-time data classification function representing equipment detection sensors, m represents the number of faulty equipment, theta represents a fault factor of the equipment, b represents a standard operating time of the equipment,
indicating the length of time the device has been operating.
The multi-source information set function has the expression:
wherein,
a multi-source information aggregation function is represented,Irepresents a multi-source information aggregation coefficient, n represents individual monitoring data, U represents the total amount of monitoring data, and->
A classification function representing information of a peripheral production line and an operator, based on the information of the peripheral production line and the operator>
Represents a threshold value set of distances between a peripheral production line, an operator and fault equipment, and>
represents the dynamic mean value of the distance between the peripheral production line, the operator and the fault equipment, and represents the distance between the peripheral production line, the operator and the fault equipment>
Representing a distance data error function;
visual imaging function, expression is:
wherein,
represents a visual imaging function, <' > based on a visual image>
Data sets which represent the operating conditions, the production speed and the production environment of the apparatus are evaluated>
Represents the resolution of the image, is greater than or equal to>
Represents a data error set representing information on the operation, production speed, production environment of the apparatus, and/or>
Representing a data error factor;
calculating the feasibility of the visual inspection, wherein the expression is as follows:
wherein,
represents the feasibility degree of the vision inspection and combines the functions of the vision inspection and the illumination inspection>
Represents the minimum visual field range in the visual inspection process and is matched>
Represents the maximum visual field range during visual inspection, e represents a natural constant and is based on the principle of the real-time detection of the real-time vision inspection, and>
an angle function representing the visual inspection.
Calculating the shortest routing inspection path of vision routing inspection, wherein the expression is as follows:
wherein,
represents the shortest routing inspection path of vision inspection and is judged>
Represents the visual inspection time and is judged>
Representing the total length of the production line, <' >>
Indicates length not visually inspected, and>
indicating the distance error of the visual inspection.
The invention provides a 5G-based industrial production line whole-process visual inspection method, which is used for visually inspecting information such as equipment, personnel and the like on an industrial production line through an AR (augmented reality) technology by using an algorithm fused by multiple models, effectively solves the problem that images and data in a machine vision system in a traditional mode are difficult to coordinate and unify, reduces the workload of managers, and accelerates the inspection speed of the industrial production line.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "fixed" are to be construed broadly, e.g., as meaning either fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made herein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. 5G-based industrial production line overall process vision inspection method is characterized by comprising the following steps:
step S1: constructing an industrial production line AR map model by utilizing an AR function and an object AR map;
step S2: performing set calculation on the distribution condition of the staff and the distribution condition of the industrial equipment in the industrial production line area displayed on the AR map by using a distribution statistical model;
and step S3: when equipment fails, acquiring real-time dynamic data of an equipment detection sensor, and positioning the specific position of the failed equipment on an AR map by using a ZigBee automatic positioning model;
and step S4: automatically analyzing various operation data around the fault equipment site, and displaying on an AR map, wherein the method comprises the following steps:
step A1: establishing a multi-source information aggregation function to calculate the running condition of a peripheral production line of the fault equipment, the information of operators and the distance between the peripheral production line of the fault equipment and the fault equipment;
step A2: displaying the on-site real-time condition of the industrial production line by using a visual imaging function, wherein the on-site real-time condition comprises equipment running condition, yield, production speed and production environment information;
step A3: a manager analyzes and makes a decision to make a visual inspection scheme and calculates the feasibility of the visual inspection;
step S5: and calculating a visual inspection shortest inspection path, displaying the visual inspection shortest inspection path on the AR map, and sending the inspection route map to a mobile equipment terminal of a manager through a wireless network so as to facilitate the manager to check the inspection route map.
2. The 5G-based industrial production line whole-process vision inspection method according to claim 1, wherein an AR map model of the industrial production line is constructed by utilizing an AR function and an object AR map, and the expression is as follows:
wherein,
representing an AR map image of an industrial production line>
A matrix of AR coefficients representing different objects on the industrial line, <' > based on the AR coefficients>
AR function representing an object>
Represents a color weight of the object>
Represents a color composition function of the object>
Represents an integration operation on the color, is asserted>
AR scale matrix representing different objects>
Representing an integral operation of the running time of the object.
3. The 5G-based visual inspection method for the whole process of the industrial production line, according to claim 1, wherein the distribution statistical model is used for carrying out set calculation on the distribution situation of the staff and the distribution situation of the industrial equipment in the area of the industrial production line displayed on the AR map, and the expression is as follows:
wherein,
represents a statistical function of the distribution on an industrial production line,. According to the present invention>
Represents a profile of the employee's position, and>
function of the number of persons representing employees at different posts>
Represents a function of the distribution of the position of the industrial installation, <' > or>
Representing a function of the number of devices on different industrial processes.
4. The 5G-based industrial production line whole-process vision inspection method according to claim 1, wherein the ZigBee automatic positioning model has an expression as follows:
wherein N represents the coordinate of the fault equipment, K represents the positioning coefficient of ZigBee, and F C A real-time data classification function representing equipment detection sensors, m represents the number of faulty equipment, theta represents a fault factor of the equipment, b represents a standard operating time of the equipment,
indicating the length of time the device has been operating. />
5. The 5G-based industrial production line whole-process vision inspection method according to claim 1, wherein the expression of the multi-source information set function is as follows:
wherein,
a multi-source information aggregation function is represented,Irepresents a multi-source information aggregation coefficient, n represents individual monitoring data, U represents the total amount of monitoring data, and->
A classification function representing information of a peripheral production line and an operator, based on the information of the peripheral production line and the operator>
Represents a threshold value set of distances between a peripheral production line, an operator and fault equipment, and>
represents the dynamic average value of the distances between the peripheral production line, the operators and the fault equipment,
representing a distance data error function;
the visual imaging function has the expression:
wherein,
represents a visual imaging function, <' > based on a visual image>
Data sets which represent the operating conditions, the production speed and the production environment of the apparatus are evaluated>
Represents the resolution of the image, is greater than or equal to>
Representing sets of data errors representing equipment operating conditions, production yields, production speeds, production environment information, and>
which is indicative of an error factor of the data,
the feasibility of the vision routing inspection is calculated, and the expression is as follows:
wherein,
represents the feasibility degree of the vision inspection and combines the functions of the vision inspection and the illumination inspection>
Represents the minimum visual field range in the visual inspection process and is matched>
Represents the maximum visual field range during visual inspection, e represents a natural constant and is based on the principle of the real-time detection of the real-time vision inspection, and>
an angle function representing the visual inspection.
6. The 5G-based industrial production line overall process visual inspection method according to claim 1, wherein the shortest inspection path is calculated according to the following expression:
wherein,
represents the shortest routing inspection path of vision inspection and is judged>
Represents the visual inspection time and is judged>
Indicates the total length of the production line and is selected>
Indicates a length not being visually inspected, and>
indicating the distance error of the visual inspection. />
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